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Scholarly Articles: How can I tell?

• Journal Information
• Literature Review
• Author and affiliation
• Introduction
• Specialized Vocabulary

Methodology

• Research sponsors
• Peer-review

The methodology section or methods section tells you how the author(s) went about doing their research. It should let you know a) what method they used to gather data (survey, interviews, experiments, etc.), why they chose this method, and what the limitations are to this method.

The methodology section should be detailed enough that another researcher could replicate the study described. When you read the methodology or methods section:

• What kind of research method did the authors use? Is it an appropriate method for the type of study they are conducting?
• How did the authors get their tests subjects? What criteria did they use?
• What are the contexts of the study that may have affected the results (e.g. environmental conditions, lab conditions, timing questions, etc.)
• Is the sample size representative of the larger population (i.e., was it big enough?)
• Are the data collection instruments and procedures likely to have measured all the important characteristics with reasonable accuracy?
• Does the data analysis appear to have been done with care, and were appropriate analytical techniques used? 

A good researcher will always let you know about the limitations of his or her research.

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What is Research Methodology? Definition, Types, and Examples

Research methodology 1,2 is a structured and scientific approach used to collect, analyze, and interpret quantitative or qualitative data to answer research questions or test hypotheses. A research methodology is like a plan for carrying out research and helps keep researchers on track by limiting the scope of the research. Several aspects must be considered before selecting an appropriate research methodology, such as research limitations and ethical concerns that may affect your research.

The research methodology section in a scientific paper describes the different methodological choices made, such as the data collection and analysis methods, and why these choices were selected. The reasons should explain why the methods chosen are the most appropriate to answer the research question. A good research methodology also helps ensure the reliability and validity of the research findings. There are three types of research methodology—quantitative, qualitative, and mixed-method, which can be chosen based on the research objectives.

What is research methodology ?

A research methodology describes the techniques and procedures used to identify and analyze information regarding a specific research topic. It is a process by which researchers design their study so that they can achieve their objectives using the selected research instruments. It includes all the important aspects of research, including research design, data collection methods, data analysis methods, and the overall framework within which the research is conducted. While these points can help you understand what is research methodology, you also need to know why it is important to pick the right methodology.

Having a good research methodology in place has the following advantages: 3

• Helps other researchers who may want to replicate your research; the explanations will be of benefit to them.
• You can easily answer any questions about your research if they arise at a later stage.
• A research methodology provides a framework and guidelines for researchers to clearly define research questions, hypotheses, and objectives.
• It helps researchers identify the most appropriate research design, sampling technique, and data collection and analysis methods.
• A sound research methodology helps researchers ensure that their findings are valid and reliable and free from biases and errors.
• It also helps ensure that ethical guidelines are followed while conducting research.
• A good research methodology helps researchers in planning their research efficiently, by ensuring optimum usage of their time and resources.

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Types of research methodology.

There are three types of research methodology based on the type of research and the data required. 1

• Quantitative research methodology focuses on measuring and testing numerical data. This approach is good for reaching a large number of people in a short amount of time. This type of research helps in testing the causal relationships between variables, making predictions, and generalizing results to wider populations.
• Qualitative research methodology examines the opinions, behaviors, and experiences of people. It collects and analyzes words and textual data. This research methodology requires fewer participants but is still more time consuming because the time spent per participant is quite large. This method is used in exploratory research where the research problem being investigated is not clearly defined.
• Mixed-method research methodology uses the characteristics of both quantitative and qualitative research methodologies in the same study. This method allows researchers to validate their findings, verify if the results observed using both methods are complementary, and explain any unexpected results obtained from one method by using the other method.

What are the types of sampling designs in research methodology?

Sampling 4 is an important part of a research methodology and involves selecting a representative sample of the population to conduct the study, making statistical inferences about them, and estimating the characteristics of the whole population based on these inferences. There are two types of sampling designs in research methodology—probability and nonprobability.

• Probability sampling

In this type of sampling design, a sample is chosen from a larger population using some form of random selection, that is, every member of the population has an equal chance of being selected. The different types of probability sampling are:

• Systematic —sample members are chosen at regular intervals. It requires selecting a starting point for the sample and sample size determination that can be repeated at regular intervals. This type of sampling method has a predefined range; hence, it is the least time consuming.
• Stratified —researchers divide the population into smaller groups that don’t overlap but represent the entire population. While sampling, these groups can be organized, and then a sample can be drawn from each group separately.
• Cluster —the population is divided into clusters based on demographic parameters like age, sex, location, etc.
• Convenience —selects participants who are most easily accessible to researchers due to geographical proximity, availability at a particular time, etc.
• Purposive —participants are selected at the researcher’s discretion. Researchers consider the purpose of the study and the understanding of the target audience.
• Snowball —already selected participants use their social networks to refer the researcher to other potential participants.
• Quota —while designing the study, the researchers decide how many people with which characteristics to include as participants. The characteristics help in choosing people most likely to provide insights into the subject.

What are data collection methods?

During research, data are collected using various methods depending on the research methodology being followed and the research methods being undertaken. Both qualitative and quantitative research have different data collection methods, as listed below.

Qualitative research 5

• One-on-one interviews: Helps the interviewers understand a respondent’s subjective opinion and experience pertaining to a specific topic or event
• Document study/literature review/record keeping: Researchers’ review of already existing written materials such as archives, annual reports, research articles, guidelines, policy documents, etc.
• Focus groups: Constructive discussions that usually include a small sample of about 6-10 people and a moderator, to understand the participants’ opinion on a given topic.
• Qualitative observation : Researchers collect data using their five senses (sight, smell, touch, taste, and hearing).

Quantitative research 6

• Sampling: The most common type is probability sampling.
• Interviews: Commonly telephonic or done in-person.
• Observations: Structured observations are most commonly used in quantitative research. In this method, researchers make observations about specific behaviors of individuals in a structured setting.
• Document review: Reviewing existing research or documents to collect evidence for supporting the research.
• Surveys and questionnaires. Surveys can be administered both online and offline depending on the requirement and sample size.

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What are data analysis methods.

The data collected using the various methods for qualitative and quantitative research need to be analyzed to generate meaningful conclusions. These data analysis methods 7 also differ between quantitative and qualitative research.

Quantitative research involves a deductive method for data analysis where hypotheses are developed at the beginning of the research and precise measurement is required. The methods include statistical analysis applications to analyze numerical data and are grouped into two categories—descriptive and inferential.

Descriptive analysis is used to describe the basic features of different types of data to present it in a way that ensures the patterns become meaningful. The different types of descriptive analysis methods are:

• Measures of frequency (count, percent, frequency)
• Measures of central tendency (mean, median, mode)
• Measures of dispersion or variation (range, variance, standard deviation)
• Measure of position (percentile ranks, quartile ranks)

Inferential analysis is used to make predictions about a larger population based on the analysis of the data collected from a smaller population. This analysis is used to study the relationships between different variables. Some commonly used inferential data analysis methods are:

• Correlation: To understand the relationship between two or more variables.
• Cross-tabulation: Analyze the relationship between multiple variables.
• Regression analysis: Study the impact of independent variables on the dependent variable.
• Frequency tables: To understand the frequency of data.
• Analysis of variance: To test the degree to which two or more variables differ in an experiment.

Qualitative research involves an inductive method for data analysis where hypotheses are developed after data collection. The methods include:

• Content analysis: For analyzing documented information from text and images by determining the presence of certain words or concepts in texts.
• Narrative analysis: For analyzing content obtained from sources such as interviews, field observations, and surveys. The stories and opinions shared by people are used to answer research questions.
• Discourse analysis: For analyzing interactions with people considering the social context, that is, the lifestyle and environment, under which the interaction occurs.
• Grounded theory: Involves hypothesis creation by data collection and analysis to explain why a phenomenon occurred.
• Thematic analysis: To identify important themes or patterns in data and use these to address an issue.

How to choose a research methodology?

Here are some important factors to consider when choosing a research methodology: 8

• Research objectives, aims, and questions —these would help structure the research design.
• Review existing literature to identify any gaps in knowledge.
• Check the statistical requirements —if data-driven or statistical results are needed then quantitative research is the best. If the research questions can be answered based on people’s opinions and perceptions, then qualitative research is most suitable.
• Sample size —sample size can often determine the feasibility of a research methodology. For a large sample, less effort- and time-intensive methods are appropriate.
• Constraints —constraints of time, geography, and resources can help define the appropriate methodology.

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How to write a research methodology .

A research methodology should include the following components: 3,9

• Research design —should be selected based on the research question and the data required. Common research designs include experimental, quasi-experimental, correlational, descriptive, and exploratory.
• Research method —this can be quantitative, qualitative, or mixed-method.
• Reason for selecting a specific methodology —explain why this methodology is the most suitable to answer your research problem.
• Research instruments —explain the research instruments you plan to use, mainly referring to the data collection methods such as interviews, surveys, etc. Here as well, a reason should be mentioned for selecting the particular instrument.
• Sampling —this involves selecting a representative subset of the population being studied.
• Data collection —involves gathering data using several data collection methods, such as surveys, interviews, etc.
• Data analysis —describe the data analysis methods you will use once you’ve collected the data.
• Research limitations —mention any limitations you foresee while conducting your research.
• Validity and reliability —validity helps identify the accuracy and truthfulness of the findings; reliability refers to the consistency and stability of the results over time and across different conditions.
• Ethical considerations —research should be conducted ethically. The considerations include obtaining consent from participants, maintaining confidentiality, and addressing conflicts of interest.

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Frequently Asked Questions

Q1. What are the key components of research methodology?

A1. A good research methodology has the following key components:

• Research design
• Data collection procedures
• Data analysis methods
• Ethical considerations

Q2. Why is ethical consideration important in research methodology?

A2. Ethical consideration is important in research methodology to ensure the readers of the reliability and validity of the study. Researchers must clearly mention the ethical norms and standards followed during the conduct of the research and also mention if the research has been cleared by any institutional board. The following 10 points are the important principles related to ethical considerations: 10

• Participants should not be subjected to harm.
• Respect for the dignity of participants should be prioritized.
• Full consent should be obtained from participants before the study.
• Participants’ privacy should be ensured.
• Confidentiality of the research data should be ensured.
• Anonymity of individuals and organizations participating in the research should be maintained.
• The aims and objectives of the research should not be exaggerated.
• Affiliations, sources of funding, and any possible conflicts of interest should be declared.
• Communication in relation to the research should be honest and transparent.
• Misleading information and biased representation of primary data findings should be avoided.

Q3. What is the difference between methodology and method?

A3. Research methodology is different from a research method, although both terms are often confused. Research methods are the tools used to gather data, while the research methodology provides a framework for how research is planned, conducted, and analyzed. The latter guides researchers in making decisions about the most appropriate methods for their research. Research methods refer to the specific techniques, procedures, and tools used by researchers to collect, analyze, and interpret data, for instance surveys, questionnaires, interviews, etc.

Research methodology is, thus, an integral part of a research study. It helps ensure that you stay on track to meet your research objectives and answer your research questions using the most appropriate data collection and analysis tools based on your research design.

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• Research methodologies. Pfeiffer Library website. Accessed August 15, 2023. https://library.tiffin.edu/researchmethodologies/whatareresearchmethodologies
• Types of research methodology. Eduvoice website. Accessed August 16, 2023. https://eduvoice.in/types-research-methodology/
• The basics of research methodology: A key to quality research. Voxco. Accessed August 16, 2023. https://www.voxco.com/blog/what-is-research-methodology/
• Sampling methods: Types with examples. QuestionPro website. Accessed August 16, 2023. https://www.questionpro.com/blog/types-of-sampling-for-social-research/
• What is qualitative research? Methods, types, approaches, examples. Researcher.Life blog. Accessed August 15, 2023. https://researcher.life/blog/article/what-is-qualitative-research-methods-types-examples/
• What is quantitative research? Definition, methods, types, and examples. Researcher.Life blog. Accessed August 15, 2023. https://researcher.life/blog/article/what-is-quantitative-research-types-and-examples/
• Data analysis in research: Types & methods. QuestionPro website. Accessed August 16, 2023. https://www.questionpro.com/blog/data-analysis-in-research/#Data_analysis_in_qualitative_research
• Factors to consider while choosing the right research methodology. PhD Monster website. Accessed August 17, 2023. https://www.phdmonster.com/factors-to-consider-while-choosing-the-right-research-methodology/
• What is research methodology? Research and writing guides. Accessed August 14, 2023. https://paperpile.com/g/what-is-research-methodology/
• Ethical considerations. Business research methodology website. Accessed August 17, 2023. https://research-methodology.net/research-methodology/ethical-considerations/

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Here's What You Need to Understand About Research Methodology

Table of Contents

Research methodology involves a systematic and well-structured approach to conducting scholarly or scientific inquiries. Knowing the significance of research methodology and its different components is crucial as it serves as the basis for any study.

Typically, your research topic will start as a broad idea you want to investigate more thoroughly. Once you’ve identified a research problem and created research questions , you must choose the appropriate methodology and frameworks to address those questions effectively.

What is the definition of a research methodology?

Research methodology is the process or the way you intend to execute your study. The methodology section of a research paper outlines how you plan to conduct your study. It covers various steps such as collecting data, statistical analysis, observing participants, and other procedures involved in the research process

The methods section should give a description of the process that will convert your idea into a study. Additionally, the outcomes of your process must provide valid and reliable results resonant with the aims and objectives of your research. This thumb rule holds complete validity, no matter whether your paper has inclinations for qualitative or quantitative usage.

Studying research methods used in related studies can provide helpful insights and direction for your own research. Now easily discover papers related to your topic on SciSpace and utilize our AI research assistant, Copilot , to quickly review the methodologies applied in different papers.

The need for a good research methodology

While deciding on your approach towards your research, the reason or factors you weighed in choosing a particular problem and formulating a research topic need to be validated and explained. A research methodology helps you do exactly that. Moreover, a good research methodology lets you build your argument to validate your research work performed through various data collection methods, analytical methods, and other essential points.

Just imagine it as a strategy documented to provide an overview of what you intend to do.

While undertaking any research writing or performing the research itself, you may get drifted in not something of much importance. In such a case, a research methodology helps you to get back to your outlined work methodology.

A research methodology helps in keeping you accountable for your work. Additionally, it can help you evaluate whether your work is in sync with your original aims and objectives or not. Besides, a good research methodology enables you to navigate your research process smoothly and swiftly while providing effective planning to achieve your desired results.

What is the basic structure of a research methodology?

Usually, you must ensure to include the following stated aspects while deciding over the basic structure of your research methodology:

1. Your research procedure

Explain what research methods you’re going to use. Whether you intend to proceed with quantitative or qualitative, or a composite of both approaches, you need to state that explicitly. The option among the three depends on your research’s aim, objectives, and scope.

2. Provide the rationality behind your chosen approach

Based on logic and reason, let your readers know why you have chosen said research methodologies. Additionally, you have to build strong arguments supporting why your chosen research method is the best way to achieve the desired outcome.

3. Explain your mechanism

The mechanism encompasses the research methods or instruments you will use to develop your research methodology. It usually refers to your data collection methods. You can use interviews, surveys, physical questionnaires, etc., of the many available mechanisms as research methodology instruments. The data collection method is determined by the type of research and whether the data is quantitative data(includes numerical data) or qualitative data (perception, morale, etc.) Moreover, you need to put logical reasoning behind choosing a particular instrument.

4. Significance of outcomes

The results will be available once you have finished experimenting. However, you should also explain how you plan to use the data to interpret the findings. This section also aids in understanding the problem from within, breaking it down into pieces, and viewing the research problem from various perspectives.

5. Reader’s advice

Anything that you feel must be explained to spread more awareness among readers and focus groups must be included and described in detail. You should not just specify your research methodology on the assumption that a reader is aware of the topic.  

All the relevant information that explains and simplifies your research paper must be included in the methodology section. If you are conducting your research in a non-traditional manner, give a logical justification and list its benefits.

6. Explain your sample space

Include information about the sample and sample space in the methodology section. The term "sample" refers to a smaller set of data that a researcher selects or chooses from a larger group of people or focus groups using a predetermined selection method. Let your readers know how you are going to distinguish between relevant and non-relevant samples. How you figured out those exact numbers to back your research methodology, i.e. the sample spacing of instruments, must be discussed thoroughly.

For example, if you are going to conduct a survey or interview, then by what procedure will you select the interviewees (or sample size in case of surveys), and how exactly will the interview or survey be conducted.

7. Challenges and limitations

This part, which is frequently assumed to be unnecessary, is actually very important. The challenges and limitations that your chosen strategy inherently possesses must be specified while you are conducting different types of research.

The importance of a good research methodology

You must have observed that all research papers, dissertations, or theses carry a chapter entirely dedicated to research methodology. This section helps maintain your credibility as a better interpreter of results rather than a manipulator.

A good research methodology always explains the procedure, data collection methods and techniques, aim, and scope of the research. In a research study, it leads to a well-organized, rationality-based approach, while the paper lacking it is often observed as messy or disorganized.

You should pay special attention to validating your chosen way towards the research methodology. This becomes extremely important in case you select an unconventional or a distinct method of execution.

Curating and developing a strong, effective research methodology can assist you in addressing a variety of situations, such as:

• When someone tries to duplicate or expand upon your research after few years.
• If a contradiction or conflict of facts occurs at a later time. This gives you the security you need to deal with these contradictions while still being able to defend your approach.
• Gaining a tactical approach in getting your research completed in time. Just ensure you are using the right approach while drafting your research methodology, and it can help you achieve your desired outcomes. Additionally, it provides a better explanation and understanding of the research question itself.
• Documenting the results so that the final outcome of the research stays as you intended it to be while starting.

Instruments you could use while writing a good research methodology

As a researcher, you must choose which tools or data collection methods that fit best in terms of the relevance of your research. This decision has to be wise.

There exists many research equipments or tools that you can use to carry out your research process. These are classified as:

a. Interviews (One-on-One or a Group)

An interview aimed to get your desired research outcomes can be undertaken in many different ways. For example, you can design your interview as structured, semi-structured, or unstructured. What sets them apart is the degree of formality in the questions. On the other hand, in a group interview, your aim should be to collect more opinions and group perceptions from the focus groups on a certain topic rather than looking out for some formal answers.

In surveys, you are in better control if you specifically draft the questions you seek the response for. For example, you may choose to include free-style questions that can be answered descriptively, or you may provide a multiple-choice type response for questions. Besides, you can also opt to choose both ways, deciding what suits your research process and purpose better.

c. Sample Groups

Similar to the group interviews, here, you can select a group of individuals and assign them a topic to discuss or freely express their opinions over that. You can simultaneously note down the answers and later draft them appropriately, deciding on the relevance of every response.

d. Observations

If your research domain is humanities or sociology, observations are the best-proven method to draw your research methodology. Of course, you can always include studying the spontaneous response of the participants towards a situation or conducting the same but in a more structured manner. A structured observation means putting the participants in a situation at a previously decided time and then studying their responses.

Of all the tools described above, it is you who should wisely choose the instruments and decide what’s the best fit for your research. You must not restrict yourself from multiple methods or a combination of a few instruments if appropriate in drafting a good research methodology.

Types of research methodology

A research methodology exists in various forms. Depending upon their approach, whether centered around words, numbers, or both, methodologies are distinguished as qualitative, quantitative, or an amalgamation of both.

1. Qualitative research methodology

When a research methodology primarily focuses on words and textual data, then it is generally referred to as qualitative research methodology. This type is usually preferred among researchers when the aim and scope of the research are mainly theoretical and explanatory.

The instruments used are observations, interviews, and sample groups. You can use this methodology if you are trying to study human behavior or response in some situations. Generally, qualitative research methodology is widely used in sociology, psychology, and other related domains.

2. Quantitative research methodology

If your research is majorly centered on data, figures, and stats, then analyzing these numerical data is often referred to as quantitative research methodology. You can use quantitative research methodology if your research requires you to validate or justify the obtained results.

In quantitative methods, surveys, tests, experiments, and evaluations of current databases can be advantageously used as instruments If your research involves testing some hypothesis, then use this methodology.

3. Amalgam methodology

As the name suggests, the amalgam methodology uses both quantitative and qualitative approaches. This methodology is used when a part of the research requires you to verify the facts and figures, whereas the other part demands you to discover the theoretical and explanatory nature of the research question.

The instruments for the amalgam methodology require you to conduct interviews and surveys, including tests and experiments. The outcome of this methodology can be insightful and valuable as it provides precise test results in line with theoretical explanations and reasoning.

The amalgam method, makes your work both factual and rational at the same time.

Final words: How to decide which is the best research methodology?

If you have kept your sincerity and awareness intact with the aims and scope of research well enough, you must have got an idea of which research methodology suits your work best.

Before deciding which research methodology answers your research question, you must invest significant time in reading and doing your homework for that. Taking references that yield relevant results should be your first approach to establishing a research methodology.

Moreover, you should never refrain from exploring other options. Before setting your work in stone, you must try all the available options as it explains why the choice of research methodology that you finally make is more appropriate than the other available options.

You should always go for a quantitative research methodology if your research requires gathering large amounts of data, figures, and statistics. This research methodology will provide you with results if your research paper involves the validation of some hypothesis.

Whereas, if  you are looking for more explanations, reasons, opinions, and public perceptions around a theory, you must use qualitative research methodology.The choice of an appropriate research methodology ultimately depends on what you want to achieve through your research.

Frequently Asked Questions (FAQs) about Research Methodology

1. how to write a research methodology.

You can always provide a separate section for research methodology where you should specify details about the methods and instruments used during the research, discussions on result analysis, including insights into the background information, and conveying the research limitations.

2. What are the types of research methodology?

There generally exists four types of research methodology i.e.

• Observation
• Experimental
• Derivational

3. What is the true meaning of research methodology?

The set of techniques or procedures followed to discover and analyze the information gathered to validate or justify a research outcome is generally called Research Methodology.

4. Where lies the importance of research methodology?

Your research methodology directly reflects the validity of your research outcomes and how well-informed your research work is. Moreover, it can help future researchers cite or refer to your research if they plan to use a similar research methodology.

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Method articles

Share methodological innovations, what is a method article .

A method article is a medium length peer-reviewed, research-focused article type that aims to answer a specific question. It also describes an advancement or development of current methodological approaches and research procedures (akin to a research article), following the standard layout for research articles.

This includes new study methods, substantive modifications to existing methods, or innovative applications of existing methods to new models or scientific questions. These should include adequate and appropriate validation to be considered, and any datasets associated with the paper must publish all experimental controls and make full datasets available.   

What are the benefits of publishing a method article?

Maximize the potential of your research by increasing the transparency, accessibility and reproducibility of your research by sharing innovations and new ways of approaching research questions.

Gain credit for your methodological innovations with a citable publication.

You can upload your method steps to protocols.io to increase the findability of your research.

How do I write a method article?

Method articles are medium length (2500-4000 words) article types following the standard layout for research articles and should include:

The abstract, introduction, method, results, and conclusions and/or discussion, within the main body of the article

A data availability statement

Author contributions

Funding/grant information

Any supplementary materials and appendices

Authors submitting methodology articles have the option to share their method steps on  protocols.io . Please note, this is not required for submission but is encouraged. 

Method article template

Here is a downloadable method article template to help you write and prepare your method article for submission: F1000Research method note template

Publishing your method article with F1000Research

F1000Research  enables you to publish your method article rapidly and openly via an author-centric platform. You’ll benefit from:

Open access publication  – for maximum visibility of your method article.

Fast publication times  – your method article will be published online with a DOI following our pre-publication checks in as little as 14 days.

Open data  – All submissions to F1000Research must comply with our open data policy and support FAIR principles.

Open peer review  – Once published, your data note will undergo a robust open peer review process where reviewers are asked specific questions about the description, methodology and availability of the data.

Please read the  guide to publishing a method article with F1000Research  and the  method article submission template , which makes it quick and easy to write your method article for submission for F1000Research.

Taylor & Francis journals

The Taylor & Francis journal portfolio offers a growing range of options for publishing method articles. To maintain quality and provide validation of the methodology and supporting information, method articles will be peer reviewed in line with the relevant submission policy. 

Below is our list of journals for publishing your method article. You can also submit a method article to Dove Medical Press journals .

Life, Earth & Environmental Sciences Please read the aims and scope of the journal to make sure its the right fit for your research

Acta Agriculturae Scandinavica, Section B — Soil & Plant Science

All Life Methods

Annals of GIS

Aquatic Insects

Archives Of Phytopathology And Plant Protection

Arid Land Research And Management

Biocatalysis and Biotransformation

Biological Rhythm Research

Bioremediation Journal

Biotechnology and Biotechnological Equipment

Canadian Journal of Plant Pathology

Carbon Management

Cell Adhesion & Migration

Communicative & Integrative Biology

Compost Science & Utilization

Epigenetics Reports

GIScience & Remote Sensing

International Journal Of Pest Management

International Journal of Sustainable Development & World Ecology

Journal of Integrative Environmental Sciences

Journal of Essential Oil Research

Journal of Herbs, Spices & Medicinal Plants

Journal of Land Use Science

Lake and Reservoir Management

Mitochondrial DNA part B

Physical Geography

Plant Signaling & Behavior

Small GTPases

Soil & Sediment Contamination: An International Journal

Studies On Neotropical Fauna And Environment

Sustainable Environment

Physical Sciences & Engineering Please read the aims and scope of the journal to make sure that it’s the right fit for your research

Aerosol Science and Technology

American Journal of Mathematical and Management Sciences

Biostatistics & Epidemiology

Brain-Computer Interfaces

Combustion Science and Technology

Composite Interfaces

Computer Methods In Biomechanics & Biomedical Engineering

Computer Methods In Biomechanics and Biomedical Engineering: Imaging & Visualization

Econometric Reviews

Experimental Heat Transfer

Enterprise Information Systems

Fullerenes, Nanotubes and Carbon Nanostructures

Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards

Information Systems Management

International Journal of Environmental Analytical Chemistry

International Biomechanics

International Journal of Computer Mathematics

International Journal of Computer Mathematics: Computer Systems Theory

International Journal of Electronics

International Journal of General Systems

International Journal of Polymer Analysis and Characterization

International Journal of Sustainable Engineering

In ternational Journal of Systems Science: Operations & Logistics

Isotopes In Environmental and Health Studies

The Journal of Adhesion

Journal of Adhesion Science and Technology

Journal of the American Society of Brewing Chemists

Journal of Dispersion Science and Technology

Journal of Biological Dynamics

Journal of Electromagnetic Waves and Applications

Journal of Medical Engineering & Technology

Journal of Intelligent Transportation Systems

Journal of Location Based Services

Journal of Organizational Computing and Electronic Commerce

Journal of Wood Chemistry and Technology

Journal of Thermal Stresses

Machining Science and Technology

Materials Research Innovations

Materials Research Letters

Mechanics Based Design of Structures and Machines

Mechanics of Advanced Materials and Structures

Nanoscale and Microscale Thermophysical Engineering

Phase Transitions

Non-Destructive Testing and Evaluation

Nucleosides, Nucleotides and Nucleic Acids

Numerical Heat Transfer, Part A: Applications

Numerical Heat Transfer, Part B: Fundamentals

Petroleum Science And Technology

Polycyclic Aromatic Compounds

Production Planning & Control

Research in Mathematics

Separation Science and Technology

Solvent Extraction and Ion Exchange

Stochastics: An International Journal Of Probability & Stochastic Processes

Supramolecular Chemistry

Medicine & Health Please read the aims and scope of the journal to make sure that it’s the right fit for your research

Annals of Human Biology

Archives Of Physiology and Biochemistry

Artificial Cells, Nanomedicine, and Biotechnology

Autoimmunity

Behavioral Sleep Medicine

Children’s Health Care

Clinical Linguistics & Phonetics

Clinical Toxicology

Cutaneous and Ocular Toxicology

Drug and Chemical Toxicology

Drug Delivery

Drug Development and Industrial Pharmacy

Electromagnetic Biology and Medicine

Endocrine Research

Expert Opinion on Orphan Drugs

Fetal and Pediatric Pathology

Growth Factors

Health, Risk & Society

HIV Research and Clinical Practice

Home Health Care Services Quarterly

Human Vaccines & Immunotherapeutics

Immunological Investigations

Immunopharmacology and Immunotoxicology

Inhalation Toxicology

International Journal of Healthcare Management

International Journal of Neuroscience

International Reviews in Immunology

Issues in Mental Health Nursing

Journal of the American Nutrition Association

Journal of Dermatological Treatment

Journal of Dietary Supplements

Journal of Drug Targeting

Journal of Enzyme Inhibition and Medicinal Chemistry

Journal of HIV/AIDS and Social Services

Journal of Immunotoxicology

Journal of Interprofessional Care

Journal of Liposome Research

Journal of Manual & Manipulative Therapy

Journal of Microencapsulation

Journal of Nutrition and Gerontology and Geriatrics

Journal of Prevention & Intervention in the Community

Journal of Oral Microbiology

Journal of Receptors and Signal Transduction

Medical Teacher

Nanotoxicology

Nutrition and Cancer

Occupational Therapy in Mental Health

Ophthalmic Genetics

Pathogens and Global Health

Pediatric Hematology & Oncology

Pharmaceutical Biology

Physical & Occupational Therapy In Geriatrics

Physical Therapy Reviews

Psychology, Health & Medicine

Seminars In Ophthalmology

Teaching and Learning in Medicine

Tissue Barriers

Toxicology Communications

Toxicology Mechanisms and Methods

Ultrastructural Pathology

Vulnerable Children and Youth Studies

Behavioral Science & Education Please read the aims and scope of the journal to make sure it’s a good fit for your research

Activities, Adaptation & Aging

Assessment & Evaluation in Higher Education

Dynamics of Asymmetric Conflict

Education 3-13

Educational Research and Evaluation

Educational Review

Irish Educational Studies

European Journal of Psychotraumatology

Health Psychology and Behavioral Medicine

Health Psychology Review

International Gambling Studies

International Journal of Bilingual Education & Bilingualism

International Journal of Early Years Education

International Journal of Adolescence and Youth

Journal of Aging & Social Policy

Journal of Child Sexual Abuse

Journal of Elder Abuse & Neglect

Journal of Evidence-Based Social Work

Journal of Geography

Journal of the History of the Neurosciences

Journal of Intergenerational Relationships

Journal of Loss and Trauma

Journal of Moral Education

Journal of Sex & Marital Therapy

Journal of School Violence

Measurement and Evaluation in Counseling and Development

Metaphor and Symbol

Military Psychology

School Psychology Review

The Journal of Positive Psychology

The Journal of Genetic Psychology

Social Sciences Please read the aims and scope of the journal to make sure it’s a good fit for your research

China Economic Journal

International Journal of Advertising

The Chinese Economy

Journal of Applied Economics

Journal of Economic Policy Reform

Political Science

Humanities, Media & Arts Please read the aims and scope of the journal to make sure it’s a good fit for your research

Critical Discourse Studies

Journal of Global Information Technology Management

Journal of Information Technology Case and Application Research

The Information Society

The journal Ecology of Food and Nutrition

Further resources

Article types.

Guide to different types of articles

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Research methods--quantitative, qualitative, and more: overview.

• Quantitative Research
• Qualitative Research
• Data Science Methods (Machine Learning, AI, Big Data)
• Text Mining and Computational Text Analysis
• Evidence Synthesis/Systematic Reviews
• Get Data, Get Help!

About Research Methods

This guide provides an overview of research methods, how to choose and use them, and supports and resources at UC Berkeley. 

As Patten and Newhart note in the book Understanding Research Methods , "Research methods are the building blocks of the scientific enterprise. They are the "how" for building systematic knowledge. The accumulation of knowledge through research is by its nature a collective endeavor. Each well-designed study provides evidence that may support, amend, refute, or deepen the understanding of existing knowledge...Decisions are important throughout the practice of research and are designed to help researchers collect evidence that includes the full spectrum of the phenomenon under study, to maintain logical rules, and to mitigate or account for possible sources of bias. In many ways, learning research methods is learning how to see and make these decisions."

The choice of methods varies by discipline, by the kind of phenomenon being studied and the data being used to study it, by the technology available, and more.  This guide is an introduction, but if you don't see what you need here, always contact your subject librarian, and/or take a look to see if there's a library research guide that will answer your question. 

Suggestions for changes and additions to this guide are welcome! 

START HERE: SAGE Research Methods

Without question, the most comprehensive resource available from the library is SAGE Research Methods.  HERE IS THE ONLINE GUIDE  to this one-stop shopping collection, and some helpful links are below:

• SAGE Research Methods
• Little Green Books  (Quantitative Methods)
• Little Blue Books  (Qualitative Methods)
• Dictionaries and Encyclopedias  
• Case studies of real research projects
• Sample datasets for hands-on practice
• Streaming video--see methods come to life
• Methodspace- -a community for researchers
• SAGE Research Methods Course Mapping

Library Data Services at UC Berkeley

Library Data Services Program and Digital Scholarship Services

The LDSP offers a variety of services and tools !  From this link, check out pages for each of the following topics:  discovering data, managing data, collecting data, GIS data, text data mining, publishing data, digital scholarship, open science, and the Research Data Management Program.

Be sure also to check out the visual guide to where to seek assistance on campus with any research question you may have!

Library GIS Services

Other Data Services at Berkeley

D-Lab Supports Berkeley faculty, staff, and graduate students with research in data intensive social science, including a wide range of training and workshop offerings Dryad Dryad is a simple self-service tool for researchers to use in publishing their datasets. It provides tools for the effective publication of and access to research data. Geospatial Innovation Facility (GIF) Provides leadership and training across a broad array of integrated mapping technologies on campu Research Data Management A UC Berkeley guide and consulting service for research data management issues

General Research Methods Resources

Here are some general resources for assistance:

• Assistance from ICPSR (must create an account to access): Getting Help with Data , and Resources for Students
• Wiley Stats Ref for background information on statistics topics
• Survey Documentation and Analysis (SDA) .  Program for easy web-based analysis of survey data.

Consultants

• D-Lab/Data Science Discovery Consultants Request help with your research project from peer consultants.
• Research data (RDM) consulting Meet with RDM consultants before designing the data security, storage, and sharing aspects of your qualitative project.
• Statistics Department Consulting Services A service in which advanced graduate students, under faculty supervision, are available to consult during specified hours in the Fall and Spring semesters.

Related Resourcex

• IRB / CPHS Qualitative research projects with human subjects often require that you go through an ethics review.
• OURS (Office of Undergraduate Research and Scholarships) OURS supports undergraduates who want to embark on research projects and assistantships. In particular, check out their "Getting Started in Research" workshops
• Sponsored Projects Sponsored projects works with researchers applying for major external grants.
• Next: Quantitative Research >>
• Last Updated: Sep 6, 2024 8:59 PM
• URL: https://guides.lib.berkeley.edu/researchmethods

Reference management. Clean and simple.

What is research methodology?

The basics of research methodology

Why do you need a research methodology, what needs to be included, why do you need to document your research method, what are the different types of research instruments, qualitative / quantitative / mixed research methodologies, how do you choose the best research methodology for you, frequently asked questions about research methodology, related articles.

When you’re working on your first piece of academic research, there are many different things to focus on, and it can be overwhelming to stay on top of everything. This is especially true of budding or inexperienced researchers.

If you’ve never put together a research proposal before or find yourself in a position where you need to explain your research methodology decisions, there are a few things you need to be aware of.

Once you understand the ins and outs, handling academic research in the future will be less intimidating. We break down the basics below:

A research methodology encompasses the way in which you intend to carry out your research. This includes how you plan to tackle things like collection methods, statistical analysis, participant observations, and more.

You can think of your research methodology as being a formula. One part will be how you plan on putting your research into practice, and another will be why you feel this is the best way to approach it. Your research methodology is ultimately a methodological and systematic plan to resolve your research problem.

In short, you are explaining how you will take your idea and turn it into a study, which in turn will produce valid and reliable results that are in accordance with the aims and objectives of your research. This is true whether your paper plans to make use of qualitative methods or quantitative methods.

The purpose of a research methodology is to explain the reasoning behind your approach to your research - you'll need to support your collection methods, methods of analysis, and other key points of your work.

Think of it like writing a plan or an outline for you what you intend to do.

When carrying out research, it can be easy to go off-track or depart from your standard methodology.

Tip: Having a methodology keeps you accountable and on track with your original aims and objectives, and gives you a suitable and sound plan to keep your project manageable, smooth, and effective.

With all that said, how do you write out your standard approach to a research methodology?

As a general plan, your methodology should include the following information:

• Your research method.  You need to state whether you plan to use quantitative analysis, qualitative analysis, or mixed-method research methods. This will often be determined by what you hope to achieve with your research.
• Explain your reasoning. Why are you taking this methodological approach? Why is this particular methodology the best way to answer your research problem and achieve your objectives?
• Explain your instruments.  This will mainly be about your collection methods. There are varying instruments to use such as interviews, physical surveys, questionnaires, for example. Your methodology will need to detail your reasoning in choosing a particular instrument for your research.
• What will you do with your results?  How are you going to analyze the data once you have gathered it?
• Advise your reader.  If there is anything in your research methodology that your reader might be unfamiliar with, you should explain it in more detail. For example, you should give any background information to your methods that might be relevant or provide your reasoning if you are conducting your research in a non-standard way.
• How will your sampling process go?  What will your sampling procedure be and why? For example, if you will collect data by carrying out semi-structured or unstructured interviews, how will you choose your interviewees and how will you conduct the interviews themselves?
• Any practical limitations?  You should discuss any limitations you foresee being an issue when you’re carrying out your research.

In any dissertation, thesis, or academic journal, you will always find a chapter dedicated to explaining the research methodology of the person who carried out the study, also referred to as the methodology section of the work.

A good research methodology will explain what you are going to do and why, while a poor methodology will lead to a messy or disorganized approach.

You should also be able to justify in this section your reasoning for why you intend to carry out your research in a particular way, especially if it might be a particularly unique method.

Having a sound methodology in place can also help you with the following:

• When another researcher at a later date wishes to try and replicate your research, they will need your explanations and guidelines.
• In the event that you receive any criticism or questioning on the research you carried out at a later point, you will be able to refer back to it and succinctly explain the how and why of your approach.
• It provides you with a plan to follow throughout your research. When you are drafting your methodology approach, you need to be sure that the method you are using is the right one for your goal. This will help you with both explaining and understanding your method.
• It affords you the opportunity to document from the outset what you intend to achieve with your research, from start to finish.

A research instrument is a tool you will use to help you collect, measure and analyze the data you use as part of your research.

The choice of research instrument will usually be yours to make as the researcher and will be whichever best suits your methodology.

There are many different research instruments you can use in collecting data for your research.

Generally, they can be grouped as follows:

• Interviews (either as a group or one-on-one). You can carry out interviews in many different ways. For example, your interview can be structured, semi-structured, or unstructured. The difference between them is how formal the set of questions is that is asked of the interviewee. In a group interview, you may choose to ask the interviewees to give you their opinions or perceptions on certain topics.
• Surveys (online or in-person). In survey research, you are posing questions in which you ask for a response from the person taking the survey. You may wish to have either free-answer questions such as essay-style questions, or you may wish to use closed questions such as multiple choice. You may even wish to make the survey a mixture of both.
• Focus Groups.  Similar to the group interview above, you may wish to ask a focus group to discuss a particular topic or opinion while you make a note of the answers given.
• Observations.  This is a good research instrument to use if you are looking into human behaviors. Different ways of researching this include studying the spontaneous behavior of participants in their everyday life, or something more structured. A structured observation is research conducted at a set time and place where researchers observe behavior as planned and agreed upon with participants.

These are the most common ways of carrying out research, but it is really dependent on your needs as a researcher and what approach you think is best to take.

It is also possible to combine a number of research instruments if this is necessary and appropriate in answering your research problem.

There are three different types of methodologies, and they are distinguished by whether they focus on words, numbers, or both.

➡️ Want to learn more about the differences between qualitative and quantitative research, and how to use both methods? Check out our guide for that!

If you've done your due diligence, you'll have an idea of which methodology approach is best suited to your research.

It’s likely that you will have carried out considerable reading and homework before you reach this point and you may have taken inspiration from other similar studies that have yielded good results.

Still, it is important to consider different options before setting your research in stone. Exploring different options available will help you to explain why the choice you ultimately make is preferable to other methods.

If proving your research problem requires you to gather large volumes of numerical data to test hypotheses, a quantitative research method is likely to provide you with the most usable results.

If instead you’re looking to try and learn more about people, and their perception of events, your methodology is more exploratory in nature and would therefore probably be better served using a qualitative research methodology.

It helps to always bring things back to the question: what do I want to achieve with my research?

Once you have conducted your research, you need to analyze it. Here are some helpful guides for qualitative data analysis:

➡️  How to do a content analysis

➡️  How to do a thematic analysis

➡️  How to do a rhetorical analysis

Research methodology refers to the techniques used to find and analyze information for a study, ensuring that the results are valid, reliable and that they address the research objective.

Data can typically be organized into four different categories or methods: observational, experimental, simulation, and derived.

Writing a methodology section is a process of introducing your methods and instruments, discussing your analysis, providing more background information, addressing your research limitations, and more.

Your research methodology section will need a clear research question and proposed research approach. You'll need to add a background, introduce your research question, write your methodology and add the works you cited during your data collecting phase.

The research methodology section of your study will indicate how valid your findings are and how well-informed your paper is. It also assists future researchers planning to use the same methodology, who want to cite your study or replicate it.

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Article Contents

Primacy of the research question, structure of the paper, writing a research article: advice to beginners.

• Article contents
• Figures & tables
• Supplementary Data

Thomas V. Perneger, Patricia M. Hudelson, Writing a research article: advice to beginners, International Journal for Quality in Health Care , Volume 16, Issue 3, June 2004, Pages 191–192, https://doi.org/10.1093/intqhc/mzh053

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Writing research papers does not come naturally to most of us. The typical research paper is a highly codified rhetorical form [ 1 , 2 ]. Knowledge of the rules—some explicit, others implied—goes a long way toward writing a paper that will get accepted in a peer-reviewed journal.

A good research paper addresses a specific research question. The research question—or study objective or main research hypothesis—is the central organizing principle of the paper. Whatever relates to the research question belongs in the paper; the rest doesn’t. This is perhaps obvious when the paper reports on a well planned research project. However, in applied domains such as quality improvement, some papers are written based on projects that were undertaken for operational reasons, and not with the primary aim of producing new knowledge. In such cases, authors should define the main research question a posteriori and design the paper around it.

Generally, only one main research question should be addressed in a paper (secondary but related questions are allowed). If a project allows you to explore several distinct research questions, write several papers. For instance, if you measured the impact of obtaining written consent on patient satisfaction at a specialized clinic using a newly developed questionnaire, you may want to write one paper on the questionnaire development and validation, and another on the impact of the intervention. The idea is not to split results into ‘least publishable units’, a practice that is rightly decried, but rather into ‘optimally publishable units’.

What is a good research question? The key attributes are: (i) specificity; (ii) originality or novelty; and (iii) general relevance to a broad scientific community. The research question should be precise and not merely identify a general area of inquiry. It can often (but not always) be expressed in terms of a possible association between X and Y in a population Z, for example ‘we examined whether providing patients about to be discharged from the hospital with written information about their medications would improve their compliance with the treatment 1 month later’. A study does not necessarily have to break completely new ground, but it should extend previous knowledge in a useful way, or alternatively refute existing knowledge. Finally, the question should be of interest to others who work in the same scientific area. The latter requirement is more challenging for those who work in applied science than for basic scientists. While it may safely be assumed that the human genome is the same worldwide, whether the results of a local quality improvement project have wider relevance requires careful consideration and argument.

Once the research question is clearly defined, writing the paper becomes considerably easier. The paper will ask the question, then answer it. The key to successful scientific writing is getting the structure of the paper right. The basic structure of a typical research paper is the sequence of Introduction, Methods, Results, and Discussion (sometimes abbreviated as IMRAD). Each section addresses a different objective. The authors state: (i) the problem they intend to address—in other terms, the research question—in the Introduction; (ii) what they did to answer the question in the Methods section; (iii) what they observed in the Results section; and (iv) what they think the results mean in the Discussion.

In turn, each basic section addresses several topics, and may be divided into subsections (Table 1 ). In the Introduction, the authors should explain the rationale and background to the study. What is the research question, and why is it important to ask it? While it is neither necessary nor desirable to provide a full-blown review of the literature as a prelude to the study, it is helpful to situate the study within some larger field of enquiry. The research question should always be spelled out, and not merely left for the reader to guess.

Typical structure of a research paper

The Methods section should provide the readers with sufficient detail about the study methods to be able to reproduce the study if so desired. Thus, this section should be specific, concrete, technical, and fairly detailed. The study setting, the sampling strategy used, instruments, data collection methods, and analysis strategies should be described. In the case of qualitative research studies, it is also useful to tell the reader which research tradition the study utilizes and to link the choice of methodological strategies with the research goals [ 3 ].

The Results section is typically fairly straightforward and factual. All results that relate to the research question should be given in detail, including simple counts and percentages. Resist the temptation to demonstrate analytic ability and the richness of the dataset by providing numerous tables of non-essential results.

The Discussion section allows the most freedom. This is why the Discussion is the most difficult to write, and is often the weakest part of a paper. Structured Discussion sections have been proposed by some journal editors [ 4 ]. While strict adherence to such rules may not be necessary, following a plan such as that proposed in Table 1 may help the novice writer stay on track.

References should be used wisely. Key assertions should be referenced, as well as the methods and instruments used. However, unless the paper is a comprehensive review of a topic, there is no need to be exhaustive. Also, references to unpublished work, to documents in the grey literature (technical reports), or to any source that the reader will have difficulty finding or understanding should be avoided.

Having the structure of the paper in place is a good start. However, there are many details that have to be attended to while writing. An obvious recommendation is to read, and follow, the instructions to authors published by the journal (typically found on the journal’s website). Another concerns non-native writers of English: do have a native speaker edit the manuscript. A paper usually goes through several drafts before it is submitted. When revising a paper, it is useful to keep an eye out for the most common mistakes (Table 2 ). If you avoid all those, your paper should be in good shape.

Common mistakes seen in manuscripts submitted to this journal

Huth EJ . How to Write and Publish Papers in the Medical Sciences , 2nd edition. Baltimore, MD: Williams & Wilkins, 1990 .

Browner WS . Publishing and Presenting Clinical Research . Baltimore, MD: Lippincott, Williams & Wilkins, 1999 .

Devers KJ , Frankel RM. Getting qualitative research published. Educ Health 2001 ; 14 : 109 –117.

Docherty M , Smith R. The case for structuring the discussion of scientific papers. Br Med J 1999 ; 318 : 1224 –1225.

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How to Write the Methods Section of a Scientific Article

What Is the Methods Section of a Research Paper?

The Methods section of a research article includes an explanation of the procedures used to conduct the experiment. For authors of scientific research papers, the objective is to present their findings clearly and concisely and to provide enough information so that the experiment can be duplicated.

Research articles contain very specific sections, usually dictated by either the target journal or specific style guides. For example, in the social and behavioral sciences, the American Psychological Association (APA) style guide is used to gather information on how the manuscript should be arranged . As with most styles, APA’s objectives are to ensure that manuscripts are written with minimum distractions to the reader. Every research article should include a detailed Methods section after the Introduction.

Why is the Methods Section Important?

The Methods section (also referred to as “Materials and Methods”) is important because it provides the reader enough information to judge whether the study is valid and reproducible.

Structure of the Methods Section in a Research Paper

While designing a research study, authors typically decide on the key points that they’re trying to prove or the “ cause-and-effect relationship ” between objects of the study. Very simply, the study is designed to meet the objective. According to APA, a Methods section comprises of the following three subsections: participants, apparatus, and procedure.

How do You Write a Method Section in Biology?

In biological sciences, the Methods section might be more detailed, but the objectives are the same—to present the study clearly and concisely so that it is understandable and can be duplicated.

If animals (including human subjects) were used in the study, authors should ensure to include statements that they were treated according to the protocols outlined to ensure that treatment is as humane as possible.

• The Declaration of Helsinki is a set of ethical principles developed by The World Medical Association to provide guidance to scientists and physicians in medical research involving human subjects.

Research conducted at an institution using human participants is overseen by the Institutional Review Board (IRB) with which it is affiliated. IRB is an administrative body whose purpose is to protect the rights and welfare of human subjects during their participation in the study.

Literature Search

Literature searches are performed to gather as much information as relevant from previous studies. They are important for providing evidence on the topic and help validate the research. Most are accomplished using keywords or phrases to search relevant databases. For example, both MEDLINE and PubMed provide information on biomedical literature. Google Scholar, according to APA, is “one of the best sources available to an individual beginning a literature search.” APA also suggests using PsycINFO and refers to it as “the premier database for locating articles in psychological science and related literature.”

Authors must make sure to have a set of keywords (usually taken from the objective statement) to stay focused and to avoid having the search move far from the original objective. Authors will benefit by setting limiting parameters, such as date ranges, and avoiding getting pulled into the trap of using non-valid resources, such as social media, conversations with people in the same discipline, or similar non-valid sources, as references.

Related: Ready with your methods section and looking forward to manuscript submission ? Check these journal selection guidelines now!

What Should be Included in the Methods Section of a Research Paper?

One commonly misused term in research papers is “methodology.” Methodology refers to a branch of the Philosophy of Science which deals with scientific methods, not to the methods themselves, so authors should avoid using it. Here is the list of main subsections that should be included in the Methods section of a research paper ; authors might use subheadings more clearly to describe their research.

• Literature search : Authors should cite any sources that helped with their choice of methods. Authors should indicate timeframes of past studies and their particular parameters.
• Study participants : Authors should cite the source from where they received any non-human subjects. The number of animals used, the ages, sex, their initial conditions, and how they were housed and cared for, should be listed. In case of human subjects, authors should provide the characteristics, such as geographical location; their age ranges, sex, and medical history (if relevant); and the number of subjects. In case hospital records were used, authors should include the subjects’ basic health information and vital statistics at the beginning of the study. Authors should also state that written informed consent was provided by each subject.
• Inclusion/exclusion criteria : Authors should describe their inclusion and exclusion criteria, how they were determined, and how many subjects were eliminated.
• Group characteristics (could be combined with “Study participants”) : Authors should describe how the chosen group was divided into subgroups and their characteristics, including the control. Authors should also describe any specific equipment used, such as housing needs and feed (usually for animal studies). If patient records are reviewed and assessed, authors should mention whether the reviewers were blinded to them.
• Procedures : Authors should describe their study design. Any necessary preparations (e.g., tissue samples, drugs) and instruments must be explained. Authors should describe how the subjects were “ manipulated to answer the experimental question .” Timeframes should be included to ensure that the procedures are clear (e.g., “Rats were given XX drug for 14 d”). For animals sacrificed, the methods used and the protocols followed should be outlined.
• Statistical analyses: The type of data, how they were measured, and which statistical tests were performed, should be described. (Note: This is not the “results” section; any relevant tables and figures should be referenced later.) Specific software used must be cited.

What Should not be Included in Your Methods Section?

Common pitfalls can make the manuscript cumbersome to read or might make the readers question the validity of the research. The University of Southern California provides some guidelines .

• Background information that is not helpful must be avoided.
• Authors must avoid providing a lot of detail.
• Authors should focus more on how their method was used to meet their objective and less on mechanics .
• Any obstacles faced and how they were overcome should be described (often in your “Study Limitations”). This will help validate the results.

According to the University of Richmond , authors must avoid including extensive details or an exhaustive list of equipment that have been used as readers could quickly lose attention. These unnecessary details add nothing to validate the research and do not help the reader understand how the objective was satisfied. A well-thought-out Methods section is one of the most important parts of the manuscript. Authors must make a note to always prepare a draft that lists all parts, allow others to review it, and revise it to remove any superfluous information.

m so confused about ma research but now m okay so thank uh so mxh

Mil gracias por su ayuda.

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• Published: 11 October 2016

Reviewing the research methods literature: principles and strategies illustrated by a systematic overview of sampling in qualitative research

• Stephen J. Gentles 1 , 4 ,
• Cathy Charles 1 ,
• David B. Nicholas 2 ,
• Jenny Ploeg 3 &
• K. Ann McKibbon 1  

Systematic Reviews volume  5 , Article number:  172 ( 2016 ) Cite this article

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Overviews of methods are potentially useful means to increase clarity and enhance collective understanding of specific methods topics that may be characterized by ambiguity, inconsistency, or a lack of comprehensiveness. This type of review represents a distinct literature synthesis method, although to date, its methodology remains relatively undeveloped despite several aspects that demand unique review procedures. The purpose of this paper is to initiate discussion about what a rigorous systematic approach to reviews of methods, referred to here as systematic methods overviews , might look like by providing tentative suggestions for approaching specific challenges likely to be encountered. The guidance offered here was derived from experience conducting a systematic methods overview on the topic of sampling in qualitative research.

The guidance is organized into several principles that highlight specific objectives for this type of review given the common challenges that must be overcome to achieve them. Optional strategies for achieving each principle are also proposed, along with discussion of how they were successfully implemented in the overview on sampling. We describe seven paired principles and strategies that address the following aspects: delimiting the initial set of publications to consider, searching beyond standard bibliographic databases, searching without the availability of relevant metadata, selecting publications on purposeful conceptual grounds, defining concepts and other information to abstract iteratively, accounting for inconsistent terminology used to describe specific methods topics, and generating rigorous verifiable analytic interpretations. Since a broad aim in systematic methods overviews is to describe and interpret the relevant literature in qualitative terms, we suggest that iterative decision making at various stages of the review process, and a rigorous qualitative approach to analysis are necessary features of this review type.

Conclusions

We believe that the principles and strategies provided here will be useful to anyone choosing to undertake a systematic methods overview. This paper represents an initial effort to promote high quality critical evaluations of the literature regarding problematic methods topics, which have the potential to promote clearer, shared understandings, and accelerate advances in research methods. Further work is warranted to develop more definitive guidance.

Peer Review reports

While reviews of methods are not new, they represent a distinct review type whose methodology remains relatively under-addressed in the literature despite the clear implications for unique review procedures. One of few examples to describe it is a chapter containing reflections of two contributing authors in a book of 21 reviews on methodological topics compiled for the British National Health Service, Health Technology Assessment Program [ 1 ]. Notable is their observation of how the differences between the methods reviews and conventional quantitative systematic reviews, specifically attributable to their varying content and purpose, have implications for defining what qualifies as systematic. While the authors describe general aspects of “systematicity” (including rigorous application of a methodical search, abstraction, and analysis), they also describe a high degree of variation within the category of methods reviews itself and so offer little in the way of concrete guidance. In this paper, we present tentative concrete guidance, in the form of a preliminary set of proposed principles and optional strategies, for a rigorous systematic approach to reviewing and evaluating the literature on quantitative or qualitative methods topics. For purposes of this article, we have used the term systematic methods overview to emphasize the notion of a systematic approach to such reviews.

The conventional focus of rigorous literature reviews (i.e., review types for which systematic methods have been codified, including the various approaches to quantitative systematic reviews [ 2 – 4 ], and the numerous forms of qualitative and mixed methods literature synthesis [ 5 – 10 ]) is to synthesize empirical research findings from multiple studies. By contrast, the focus of overviews of methods, including the systematic approach we advocate, is to synthesize guidance on methods topics. The literature consulted for such reviews may include the methods literature, methods-relevant sections of empirical research reports, or both. Thus, this paper adds to previous work published in this journal—namely, recent preliminary guidance for conducting reviews of theory [ 11 ]—that has extended the application of systematic review methods to novel review types that are concerned with subject matter other than empirical research findings.

Published examples of methods overviews illustrate the varying objectives they can have. One objective is to establish methodological standards for appraisal purposes. For example, reviews of existing quality appraisal standards have been used to propose universal standards for appraising the quality of primary qualitative research [ 12 ] or evaluating qualitative research reports [ 13 ]. A second objective is to survey the methods-relevant sections of empirical research reports to establish current practices on methods use and reporting practices, which Moher and colleagues [ 14 ] recommend as a means for establishing the needs to be addressed in reporting guidelines (see, for example [ 15 , 16 ]). A third objective for a methods review is to offer clarity and enhance collective understanding regarding a specific methods topic that may be characterized by ambiguity, inconsistency, or a lack of comprehensiveness within the available methods literature. An example of this is a overview whose objective was to review the inconsistent definitions of intention-to-treat analysis (the methodologically preferred approach to analyze randomized controlled trial data) that have been offered in the methods literature and propose a solution for improving conceptual clarity [ 17 ]. Such reviews are warranted because students and researchers who must learn or apply research methods typically lack the time to systematically search, retrieve, review, and compare the available literature to develop a thorough and critical sense of the varied approaches regarding certain controversial or ambiguous methods topics.

While systematic methods overviews , as a review type, include both reviews of the methods literature and reviews of methods-relevant sections from empirical study reports, the guidance provided here is primarily applicable to reviews of the methods literature since it was derived from the experience of conducting such a review [ 18 ], described below. To our knowledge, there are no well-developed proposals on how to rigorously conduct such reviews. Such guidance would have the potential to improve the thoroughness and credibility of critical evaluations of the methods literature, which could increase their utility as a tool for generating understandings that advance research methods, both qualitative and quantitative. Our aim in this paper is thus to initiate discussion about what might constitute a rigorous approach to systematic methods overviews. While we hope to promote rigor in the conduct of systematic methods overviews wherever possible, we do not wish to suggest that all methods overviews need be conducted to the same standard. Rather, we believe that the level of rigor may need to be tailored pragmatically to the specific review objectives, which may not always justify the resource requirements of an intensive review process.

The example systematic methods overview on sampling in qualitative research

The principles and strategies we propose in this paper are derived from experience conducting a systematic methods overview on the topic of sampling in qualitative research [ 18 ]. The main objective of that methods overview was to bring clarity and deeper understanding of the prominent concepts related to sampling in qualitative research (purposeful sampling strategies, saturation, etc.). Specifically, we interpreted the available guidance, commenting on areas lacking clarity, consistency, or comprehensiveness (without proposing any recommendations on how to do sampling). This was achieved by a comparative and critical analysis of publications representing the most influential (i.e., highly cited) guidance across several methodological traditions in qualitative research.

The specific methods and procedures for the overview on sampling [ 18 ] from which our proposals are derived were developed both after soliciting initial input from local experts in qualitative research and an expert health librarian (KAM) and through ongoing careful deliberation throughout the review process. To summarize, in that review, we employed a transparent and rigorous approach to search the methods literature, selected publications for inclusion according to a purposeful and iterative process, abstracted textual data using structured abstraction forms, and analyzed (synthesized) the data using a systematic multi-step approach featuring abstraction of text, summary of information in matrices, and analytic comparisons.

For this article, we reflected on both the problems and challenges encountered at different stages of the review and our means for selecting justifiable procedures to deal with them. Several principles were then derived by considering the generic nature of these problems, while the generalizable aspects of the procedures used to address them formed the basis of optional strategies. Further details of the specific methods and procedures used in the overview on qualitative sampling are provided below to illustrate both the types of objectives and challenges that reviewers will likely need to consider and our approach to implementing each of the principles and strategies.

Organization of the guidance into principles and strategies

For the purposes of this article, principles are general statements outlining what we propose are important aims or considerations within a particular review process, given the unique objectives or challenges to be overcome with this type of review. These statements follow the general format, “considering the objective or challenge of X, we propose Y to be an important aim or consideration.” Strategies are optional and flexible approaches for implementing the previous principle outlined. Thus, generic challenges give rise to principles, which in turn give rise to strategies.

We organize the principles and strategies below into three sections corresponding to processes characteristic of most systematic literature synthesis approaches: literature identification and selection ; data abstraction from the publications selected for inclusion; and analysis , including critical appraisal and synthesis of the abstracted data. Within each section, we also describe the specific methodological decisions and procedures used in the overview on sampling in qualitative research [ 18 ] to illustrate how the principles and strategies for each review process were applied and implemented in a specific case. We expect this guidance and accompanying illustrations will be useful for anyone considering engaging in a methods overview, particularly those who may be familiar with conventional systematic review methods but may not yet appreciate some of the challenges specific to reviewing the methods literature.

Results and discussion

Literature identification and selection.

The identification and selection process includes search and retrieval of publications and the development and application of inclusion and exclusion criteria to select the publications that will be abstracted and analyzed in the final review. Literature identification and selection for overviews of the methods literature is challenging and potentially more resource-intensive than for most reviews of empirical research. This is true for several reasons that we describe below, alongside discussion of the potential solutions. Additionally, we suggest in this section how the selection procedures can be chosen to match the specific analytic approach used in methods overviews.

Delimiting a manageable set of publications

One aspect of methods overviews that can make identification and selection challenging is the fact that the universe of literature containing potentially relevant information regarding most methods-related topics is expansive and often unmanageably so. Reviewers are faced with two large categories of literature: the methods literature , where the possible publication types include journal articles, books, and book chapters; and the methods-relevant sections of empirical study reports , where the possible publication types include journal articles, monographs, books, theses, and conference proceedings. In our systematic overview of sampling in qualitative research, exhaustively searching (including retrieval and first-pass screening) all publication types across both categories of literature for information on a single methods-related topic was too burdensome to be feasible. The following proposed principle follows from the need to delimit a manageable set of literature for the review.

Principle #1:

Considering the broad universe of potentially relevant literature, we propose that an important objective early in the identification and selection stage is to delimit a manageable set of methods-relevant publications in accordance with the objectives of the methods overview.

Strategy #1:

To limit the set of methods-relevant publications that must be managed in the selection process, reviewers have the option to initially review only the methods literature, and exclude the methods-relevant sections of empirical study reports, provided this aligns with the review’s particular objectives.

We propose that reviewers are justified in choosing to select only the methods literature when the objective is to map out the range of recognized concepts relevant to a methods topic, to summarize the most authoritative or influential definitions or meanings for methods-related concepts, or to demonstrate a problematic lack of clarity regarding a widely established methods-related concept and potentially make recommendations for a preferred approach to the methods topic in question. For example, in the case of the methods overview on sampling [ 18 ], the primary aim was to define areas lacking in clarity for multiple widely established sampling-related topics. In the review on intention-to-treat in the context of missing outcome data [ 17 ], the authors identified a lack of clarity based on multiple inconsistent definitions in the literature and went on to recommend separating the issue of how to handle missing outcome data from the issue of whether an intention-to-treat analysis can be claimed.

In contrast to strategy #1, it may be appropriate to select the methods-relevant sections of empirical study reports when the objective is to illustrate how a methods concept is operationalized in research practice or reported by authors. For example, one could review all the publications in 2 years’ worth of issues of five high-impact field-related journals to answer questions about how researchers describe implementing a particular method or approach, or to quantify how consistently they define or report using it. Such reviews are often used to highlight gaps in the reporting practices regarding specific methods, which may be used to justify items to address in reporting guidelines (for example, [ 14 – 16 ]).

It is worth recognizing that other authors have advocated broader positions regarding the scope of literature to be considered in a review, expanding on our perspective. Suri [ 10 ] (who, like us, emphasizes how different sampling strategies are suitable for different literature synthesis objectives) has, for example, described a two-stage literature sampling procedure (pp. 96–97). First, reviewers use an initial approach to conduct a broad overview of the field—for reviews of methods topics, this would entail an initial review of the research methods literature. This is followed by a second more focused stage in which practical examples are purposefully selected—for methods reviews, this would involve sampling the empirical literature to illustrate key themes and variations. While this approach is seductive in its capacity to generate more in depth and interpretive analytic findings, some reviewers may consider it too resource-intensive to include the second step no matter how selective the purposeful sampling. In the overview on sampling where we stopped after the first stage [ 18 ], we discussed our selective focus on the methods literature as a limitation that left opportunities for further analysis of the literature. We explicitly recommended, for example, that theoretical sampling was a topic for which a future review of the methods sections of empirical reports was justified to answer specific questions identified in the primary review.

Ultimately, reviewers must make pragmatic decisions that balance resource considerations, combined with informed predictions about the depth and complexity of literature available on their topic, with the stated objectives of their review. The remaining principles and strategies apply primarily to overviews that include the methods literature, although some aspects may be relevant to reviews that include empirical study reports.

Searching beyond standard bibliographic databases

An important reality affecting identification and selection in overviews of the methods literature is the increased likelihood for relevant publications to be located in sources other than journal articles (which is usually not the case for overviews of empirical research, where journal articles generally represent the primary publication type). In the overview on sampling [ 18 ], out of 41 full-text publications retrieved and reviewed, only 4 were journal articles, while 37 were books or book chapters. Since many books and book chapters did not exist electronically, their full text had to be physically retrieved in hardcopy, while 11 publications were retrievable only through interlibrary loan or purchase request. The tasks associated with such retrieval are substantially more time-consuming than electronic retrieval. Since a substantial proportion of methods-related guidance may be located in publication types that are less comprehensively indexed in standard bibliographic databases, identification and retrieval thus become complicated processes.

Principle #2:

Considering that important sources of methods guidance can be located in non-journal publication types (e.g., books, book chapters) that tend to be poorly indexed in standard bibliographic databases, it is important to consider alternative search methods for identifying relevant publications to be further screened for inclusion.

Strategy #2:

To identify books, book chapters, and other non-journal publication types not thoroughly indexed in standard bibliographic databases, reviewers may choose to consult one or more of the following less standard sources: Google Scholar, publisher web sites, or expert opinion.

In the case of the overview on sampling in qualitative research [ 18 ], Google Scholar had two advantages over other standard bibliographic databases: it indexes and returns records of books and book chapters likely to contain guidance on qualitative research methods topics; and it has been validated as providing higher citation counts than ISI Web of Science (a producer of numerous bibliographic databases accessible through institutional subscription) for several non-biomedical disciplines including the social sciences where qualitative research methods are prominently used [ 19 – 21 ]. While we identified numerous useful publications by consulting experts, the author publication lists generated through Google Scholar searches were uniquely useful to identify more recent editions of methods books identified by experts.

Searching without relevant metadata

Determining what publications to select for inclusion in the overview on sampling [ 18 ] could only rarely be accomplished by reviewing the publication’s metadata. This was because for the many books and other non-journal type publications we identified as possibly relevant, the potential content of interest would be located in only a subsection of the publication. In this common scenario for reviews of the methods literature (as opposed to methods overviews that include empirical study reports), reviewers will often be unable to employ standard title, abstract, and keyword database searching or screening as a means for selecting publications.

Principle #3:

Considering that the presence of information about the topic of interest may not be indicated in the metadata for books and similar publication types, it is important to consider other means of identifying potentially useful publications for further screening.

Strategy #3:

One approach to identifying potentially useful books and similar publication types is to consider what classes of such publications (e.g., all methods manuals for a certain research approach) are likely to contain relevant content, then identify, retrieve, and review the full text of corresponding publications to determine whether they contain information on the topic of interest.

In the example of the overview on sampling in qualitative research [ 18 ], the topic of interest (sampling) was one of numerous topics covered in the general qualitative research methods manuals. Consequently, examples from this class of publications first had to be identified for retrieval according to non-keyword-dependent criteria. Thus, all methods manuals within the three research traditions reviewed (grounded theory, phenomenology, and case study) that might contain discussion of sampling were sought through Google Scholar and expert opinion, their full text obtained, and hand-searched for relevant content to determine eligibility. We used tables of contents and index sections of books to aid this hand searching.

Purposefully selecting literature on conceptual grounds

A final consideration in methods overviews relates to the type of analysis used to generate the review findings. Unlike quantitative systematic reviews where reviewers aim for accurate or unbiased quantitative estimates—something that requires identifying and selecting the literature exhaustively to obtain all relevant data available (i.e., a complete sample)—in methods overviews, reviewers must describe and interpret the relevant literature in qualitative terms to achieve review objectives. In other words, the aim in methods overviews is to seek coverage of the qualitative concepts relevant to the methods topic at hand. For example, in the overview of sampling in qualitative research [ 18 ], achieving review objectives entailed providing conceptual coverage of eight sampling-related topics that emerged as key domains. The following principle recognizes that literature sampling should therefore support generating qualitative conceptual data as the input to analysis.

Principle #4:

Since the analytic findings of a systematic methods overview are generated through qualitative description and interpretation of the literature on a specified topic, selection of the literature should be guided by a purposeful strategy designed to achieve adequate conceptual coverage (i.e., representing an appropriate degree of variation in relevant ideas) of the topic according to objectives of the review.

Strategy #4:

One strategy for choosing the purposeful approach to use in selecting the literature according to the review objectives is to consider whether those objectives imply exploring concepts either at a broad overview level, in which case combining maximum variation selection with a strategy that limits yield (e.g., critical case, politically important, or sampling for influence—described below) may be appropriate; or in depth, in which case purposeful approaches aimed at revealing innovative cases will likely be necessary.

In the methods overview on sampling, the implied scope was broad since we set out to review publications on sampling across three divergent qualitative research traditions—grounded theory, phenomenology, and case study—to facilitate making informative conceptual comparisons. Such an approach would be analogous to maximum variation sampling.

At the same time, the purpose of that review was to critically interrogate the clarity, consistency, and comprehensiveness of literature from these traditions that was “most likely to have widely influenced students’ and researchers’ ideas about sampling” (p. 1774) [ 18 ]. In other words, we explicitly set out to review and critique the most established and influential (and therefore dominant) literature, since this represents a common basis of knowledge among students and researchers seeking understanding or practical guidance on sampling in qualitative research. To achieve this objective, we purposefully sampled publications according to the criterion of influence , which we operationalized as how often an author or publication has been referenced in print or informal discourse. This second sampling approach also limited the literature we needed to consider within our broad scope review to a manageable amount.

To operationalize this strategy of sampling for influence , we sought to identify both the most influential authors within a qualitative research tradition (all of whose citations were subsequently screened) and the most influential publications on the topic of interest by non-influential authors. This involved a flexible approach that combined multiple indicators of influence to avoid the dilemma that any single indicator might provide inadequate coverage. These indicators included bibliometric data (h-index for author influence [ 22 ]; number of cites for publication influence), expert opinion, and cross-references in the literature (i.e., snowball sampling). As a final selection criterion, a publication was included only if it made an original contribution in terms of novel guidance regarding sampling or a related concept; thus, purely secondary sources were excluded. Publish or Perish software (Anne-Wil Harzing; available at http://www.harzing.com/resources/publish-or-perish ) was used to generate bibliometric data via the Google Scholar database. Figure  1 illustrates how identification and selection in the methods overview on sampling was a multi-faceted and iterative process. The authors selected as influential, and the publications selected for inclusion or exclusion are listed in Additional file 1 (Matrices 1, 2a, 2b).

Literature identification and selection process used in the methods overview on sampling [ 18 ]

In summary, the strategies of seeking maximum variation and sampling for influence were employed in the sampling overview to meet the specific review objectives described. Reviewers will need to consider the full range of purposeful literature sampling approaches at their disposal in deciding what best matches the specific aims of their own reviews. Suri [ 10 ] has recently retooled Patton’s well-known typology of purposeful sampling strategies (originally intended for primary research) for application to literature synthesis, providing a useful resource in this respect.

Data abstraction

The purpose of data abstraction in rigorous literature reviews is to locate and record all data relevant to the topic of interest from the full text of included publications, making them available for subsequent analysis. Conventionally, a data abstraction form—consisting of numerous distinct conceptually defined fields to which corresponding information from the source publication is recorded—is developed and employed. There are several challenges, however, to the processes of developing the abstraction form and abstracting the data itself when conducting methods overviews, which we address here. Some of these problems and their solutions may be familiar to those who have conducted qualitative literature syntheses, which are similarly conceptual.

Iteratively defining conceptual information to abstract

In the overview on sampling [ 18 ], while we surveyed multiple sources beforehand to develop a list of concepts relevant for abstraction (e.g., purposeful sampling strategies, saturation, sample size), there was no way for us to anticipate some concepts prior to encountering them in the review process. Indeed, in many cases, reviewers are unable to determine the complete set of methods-related concepts that will be the focus of the final review a priori without having systematically reviewed the publications to be included. Thus, defining what information to abstract beforehand may not be feasible.

Principle #5:

Considering the potential impracticality of defining a complete set of relevant methods-related concepts from a body of literature one has not yet systematically read, selecting and defining fields for data abstraction must often be undertaken iteratively. Thus, concepts to be abstracted can be expected to grow and change as data abstraction proceeds.

Strategy #5:

Reviewers can develop an initial form or set of concepts for abstraction purposes according to standard methods (e.g., incorporating expert feedback, pilot testing) and remain attentive to the need to iteratively revise it as concepts are added or modified during the review. Reviewers should document revisions and return to re-abstract data from previously abstracted publications as the new data requirements are determined.

In the sampling overview [ 18 ], we developed and maintained the abstraction form in Microsoft Word. We derived the initial set of abstraction fields from our own knowledge of relevant sampling-related concepts, consultation with local experts, and reviewing a pilot sample of publications. Since the publications in this review included a large proportion of books, the abstraction process often began by flagging the broad sections within a publication containing topic-relevant information for detailed review to identify text to abstract. When reviewing flagged text, the reviewer occasionally encountered an unanticipated concept significant enough to warrant being added as a new field to the abstraction form. For example, a field was added to capture how authors described the timing of sampling decisions, whether before (a priori) or after (ongoing) starting data collection, or whether this was unclear. In these cases, we systematically documented the modification to the form and returned to previously abstracted publications to abstract any information that might be relevant to the new field.

The logic of this strategy is analogous to the logic used in a form of research synthesis called best fit framework synthesis (BFFS) [ 23 – 25 ]. In that method, reviewers initially code evidence using an a priori framework they have selected. When evidence cannot be accommodated by the selected framework, reviewers then develop new themes or concepts from which they construct a new expanded framework. Both the strategy proposed and the BFFS approach to research synthesis are notable for their rigorous and transparent means to adapt a final set of concepts to the content under review.

Accounting for inconsistent terminology

An important complication affecting the abstraction process in methods overviews is that the language used by authors to describe methods-related concepts can easily vary across publications. For example, authors from different qualitative research traditions often use different terms for similar methods-related concepts. Furthermore, as we found in the sampling overview [ 18 ], there may be cases where no identifiable term, phrase, or label for a methods-related concept is used at all, and a description of it is given instead. This can make searching the text for relevant concepts based on keywords unreliable.

Principle #6:

Since accepted terms may not be used consistently to refer to methods concepts, it is necessary to rely on the definitions for concepts, rather than keywords, to identify relevant information in the publication to abstract.

Strategy #6:

An effective means to systematically identify relevant information is to develop and iteratively adjust written definitions for key concepts (corresponding to abstraction fields) that are consistent with and as inclusive of as much of the literature reviewed as possible. Reviewers then seek information that matches these definitions (rather than keywords) when scanning a publication for relevant data to abstract.

In the abstraction process for the sampling overview [ 18 ], we noted the several concepts of interest to the review for which abstraction by keyword was particularly problematic due to inconsistent terminology across publications: sampling , purposeful sampling , sampling strategy , and saturation (for examples, see Additional file 1 , Matrices 3a, 3b, 4). We iteratively developed definitions for these concepts by abstracting text from publications that either provided an explicit definition or from which an implicit definition could be derived, which was recorded in fields dedicated to the concept’s definition. Using a method of constant comparison, we used text from definition fields to inform and modify a centrally maintained definition of the corresponding concept to optimize its fit and inclusiveness with the literature reviewed. Table  1 shows, as an example, the final definition constructed in this way for one of the central concepts of the review, qualitative sampling .

We applied iteratively developed definitions when making decisions about what specific text to abstract for an existing field, which allowed us to abstract concept-relevant data even if no recognized keyword was used. For example, this was the case for the sampling-related concept, saturation , where the relevant text available for abstraction in one publication [ 26 ]—“to continue to collect data until nothing new was being observed or recorded, no matter how long that takes”—was not accompanied by any term or label whatsoever.

This comparative analytic strategy (and our approach to analysis more broadly as described in strategy #7, below) is analogous to the process of reciprocal translation —a technique first introduced for meta-ethnography by Noblit and Hare [ 27 ] that has since been recognized as a common element in a variety of qualitative metasynthesis approaches [ 28 ]. Reciprocal translation, taken broadly, involves making sense of a study’s findings in terms of the findings of the other studies included in the review. In practice, it has been operationalized in different ways. Melendez-Torres and colleagues developed a typology from their review of the metasynthesis literature, describing four overlapping categories of specific operations undertaken in reciprocal translation: visual representation, key paper integration, data reduction and thematic extraction, and line-by-line coding [ 28 ]. The approaches suggested in both strategies #6 and #7, with their emphasis on constant comparison, appear to fall within the line-by-line coding category.

Generating credible and verifiable analytic interpretations

The analysis in a systematic methods overview must support its more general objective, which we suggested above is often to offer clarity and enhance collective understanding regarding a chosen methods topic. In our experience, this involves describing and interpreting the relevant literature in qualitative terms. Furthermore, any interpretative analysis required may entail reaching different levels of abstraction, depending on the more specific objectives of the review. For example, in the overview on sampling [ 18 ], we aimed to produce a comparative analysis of how multiple sampling-related topics were treated differently within and among different qualitative research traditions. To promote credibility of the review, however, not only should one seek a qualitative analytic approach that facilitates reaching varying levels of abstraction but that approach must also ensure that abstract interpretations are supported and justified by the source data and not solely the product of the analyst’s speculative thinking.

Principle #7:

Considering the qualitative nature of the analysis required in systematic methods overviews, it is important to select an analytic method whose interpretations can be verified as being consistent with the literature selected, regardless of the level of abstraction reached.

Strategy #7:

We suggest employing the constant comparative method of analysis [ 29 ] because it supports developing and verifying analytic links to the source data throughout progressively interpretive or abstract levels. In applying this approach, we advise a rigorous approach, documenting how supportive quotes or references to the original texts are carried forward in the successive steps of analysis to allow for easy verification.

The analytic approach used in the methods overview on sampling [ 18 ] comprised four explicit steps, progressing in level of abstraction—data abstraction, matrices, narrative summaries, and final analytic conclusions (Fig.  2 ). While we have positioned data abstraction as the second stage of the generic review process (prior to Analysis), above, we also considered it as an initial step of analysis in the sampling overview for several reasons. First, it involved a process of constant comparisons and iterative decision-making about the fields to add or define during development and modification of the abstraction form, through which we established the range of concepts to be addressed in the review. At the same time, abstraction involved continuous analytic decisions about what textual quotes (ranging in size from short phrases to numerous paragraphs) to record in the fields thus created. This constant comparative process was analogous to open coding in which textual data from publications was compared to conceptual fields (equivalent to codes) or to other instances of data previously abstracted when constructing definitions to optimize their fit with the overall literature as described in strategy #6. Finally, in the data abstraction step, we also recorded our first interpretive thoughts in dedicated fields, providing initial material for the more abstract analytic steps.

Summary of progressive steps of analysis used in the methods overview on sampling [ 18 ]

In the second step of the analysis, we constructed topic-specific matrices , or tables, by copying relevant quotes from abstraction forms into the appropriate cells of matrices (for the complete set of analytic matrices developed in the sampling review, see Additional file 1 (matrices 3 to 10)). Each matrix ranged from one to five pages; row headings, nested three-deep, identified the methodological tradition, author, and publication, respectively; and column headings identified the concepts, which corresponded to abstraction fields. Matrices thus allowed us to make further comparisons across methodological traditions, and between authors within a tradition. In the third step of analysis, we recorded our comparative observations as narrative summaries , in which we used illustrative quotes more sparingly. In the final step, we developed analytic conclusions based on the narrative summaries about the sampling-related concepts within each methodological tradition for which clarity, consistency, or comprehensiveness of the available guidance appeared to be lacking. Higher levels of analysis thus built logically from the lower levels, enabling us to easily verify analytic conclusions by tracing the support for claims by comparing the original text of publications reviewed.

Integrative versus interpretive methods overviews

The analytic product of systematic methods overviews is comparable to qualitative evidence syntheses, since both involve describing and interpreting the relevant literature in qualitative terms. Most qualitative synthesis approaches strive to produce new conceptual understandings that vary in level of interpretation. Dixon-Woods and colleagues [ 30 ] elaborate on a useful distinction, originating from Noblit and Hare [ 27 ], between integrative and interpretive reviews. Integrative reviews focus on summarizing available primary data and involve using largely secure and well defined concepts to do so; definitions are used from an early stage to specify categories for abstraction (or coding) of data, which in turn supports their aggregation; they do not seek as their primary focus to develop or specify new concepts, although they may achieve some theoretical or interpretive functions. For interpretive reviews, meanwhile, the main focus is to develop new concepts and theories that integrate them, with the implication that the concepts developed become fully defined towards the end of the analysis. These two forms are not completely distinct, and “every integrative synthesis will include elements of interpretation, and every interpretive synthesis will include elements of aggregation of data” [ 30 ].

The example methods overview on sampling [ 18 ] could be classified as predominantly integrative because its primary goal was to aggregate influential authors’ ideas on sampling-related concepts; there were also, however, elements of interpretive synthesis since it aimed to develop new ideas about where clarity in guidance on certain sampling-related topics is lacking, and definitions for some concepts were flexible and not fixed until late in the review. We suggest that most systematic methods overviews will be classifiable as predominantly integrative (aggregative). Nevertheless, more highly interpretive methods overviews are also quite possible—for example, when the review objective is to provide a highly critical analysis for the purpose of generating new methodological guidance. In such cases, reviewers may need to sample more deeply (see strategy #4), specifically by selecting empirical research reports (i.e., to go beyond dominant or influential ideas in the methods literature) that are likely to feature innovations or instructive lessons in employing a given method.

In this paper, we have outlined tentative guidance in the form of seven principles and strategies on how to conduct systematic methods overviews, a review type in which methods-relevant literature is systematically analyzed with the aim of offering clarity and enhancing collective understanding regarding a specific methods topic. Our proposals include strategies for delimiting the set of publications to consider, searching beyond standard bibliographic databases, searching without the availability of relevant metadata, selecting publications on purposeful conceptual grounds, defining concepts and other information to abstract iteratively, accounting for inconsistent terminology, and generating credible and verifiable analytic interpretations. We hope the suggestions proposed will be useful to others undertaking reviews on methods topics in future.

As far as we are aware, this is the first published source of concrete guidance for conducting this type of review. It is important to note that our primary objective was to initiate methodological discussion by stimulating reflection on what rigorous methods for this type of review should look like, leaving the development of more complete guidance to future work. While derived from the experience of reviewing a single qualitative methods topic, we believe the principles and strategies provided are generalizable to overviews of both qualitative and quantitative methods topics alike. However, it is expected that additional challenges and insights for conducting such reviews have yet to be defined. Thus, we propose that next steps for developing more definitive guidance should involve an attempt to collect and integrate other reviewers’ perspectives and experiences in conducting systematic methods overviews on a broad range of qualitative and quantitative methods topics. Formalized guidance and standards would improve the quality of future methods overviews, something we believe has important implications for advancing qualitative and quantitative methodology. When undertaken to a high standard, rigorous critical evaluations of the available methods guidance have significant potential to make implicit controversies explicit, and improve the clarity and precision of our understandings of problematic qualitative or quantitative methods issues.

A review process central to most types of rigorous reviews of empirical studies, which we did not explicitly address in a separate review step above, is quality appraisal . The reason we have not treated this as a separate step stems from the different objectives of the primary publications included in overviews of the methods literature (i.e., providing methodological guidance) compared to the primary publications included in the other established review types (i.e., reporting findings from single empirical studies). This is not to say that appraising quality of the methods literature is not an important concern for systematic methods overviews. Rather, appraisal is much more integral to (and difficult to separate from) the analysis step, in which we advocate appraising clarity, consistency, and comprehensiveness—the quality appraisal criteria that we suggest are appropriate for the methods literature. As a second important difference regarding appraisal, we currently advocate appraising the aforementioned aspects at the level of the literature in aggregate rather than at the level of individual publications. One reason for this is that methods guidance from individual publications generally builds on previous literature, and thus we feel that ahistorical judgments about comprehensiveness of single publications lack relevance and utility. Additionally, while different methods authors may express themselves less clearly than others, their guidance can nonetheless be highly influential and useful, and should therefore not be downgraded or ignored based on considerations of clarity—which raises questions about the alternative uses that quality appraisals of individual publications might have. Finally, legitimate variability in the perspectives that methods authors wish to emphasize, and the levels of generality at which they write about methods, makes critiquing individual publications based on the criterion of clarity a complex and potentially problematic endeavor that is beyond the scope of this paper to address. By appraising the current state of the literature at a holistic level, reviewers stand to identify important gaps in understanding that represent valuable opportunities for further methodological development.

To summarize, the principles and strategies provided here may be useful to those seeking to undertake their own systematic methods overview. Additional work is needed, however, to establish guidance that is comprehensive by comparing the experiences from conducting a variety of methods overviews on a range of methods topics. Efforts that further advance standards for systematic methods overviews have the potential to promote high-quality critical evaluations that produce conceptually clear and unified understandings of problematic methods topics, thereby accelerating the advance of research methodology.

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SJG wrote the first draft of this article, with CC contributing to drafting. All authors contributed to revising the manuscript. All authors except CC (deceased) approved the final draft. SJG, CC, KAB, and JP were involved in developing methods for the systematic methods overview on sampling.

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Gentles, S.J., Charles, C., Nicholas, D.B. et al. Reviewing the research methods literature: principles and strategies illustrated by a systematic overview of sampling in qualitative research. Syst Rev 5 , 172 (2016). https://doi.org/10.1186/s13643-016-0343-0

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A tutorial on methodological studies: the what, when, how and why

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Methodological studies – studies that evaluate the design, analysis or reporting of other research-related reports – play an important role in health research. They help to highlight issues in the conduct of research with the aim of improving health research methodology, and ultimately reducing research waste.

We provide an overview of some of the key aspects of methodological studies such as what they are, and when, how and why they are done. We adopt a “frequently asked questions” format to facilitate reading this paper and provide multiple examples to help guide researchers interested in conducting methodological studies. Some of the topics addressed include: is it necessary to publish a study protocol? How to select relevant research reports and databases for a methodological study? What approaches to data extraction and statistical analysis should be considered when conducting a methodological study? What are potential threats to validity and is there a way to appraise the quality of methodological studies?

Appropriate reflection and application of basic principles of epidemiology and biostatistics are required in the design and analysis of methodological studies. This paper provides an introduction for further discussion about the conduct of methodological studies.

Peer Review reports

The field of meta-research (or research-on-research) has proliferated in recent years in response to issues with research quality and conduct [ 1 , 2 , 3 ]. As the name suggests, this field targets issues with research design, conduct, analysis and reporting. Various types of research reports are often examined as the unit of analysis in these studies (e.g. abstracts, full manuscripts, trial registry entries). Like many other novel fields of research, meta-research has seen a proliferation of use before the development of reporting guidance. For example, this was the case with randomized trials for which risk of bias tools and reporting guidelines were only developed much later – after many trials had been published and noted to have limitations [ 4 , 5 ]; and for systematic reviews as well [ 6 , 7 , 8 ]. However, in the absence of formal guidance, studies that report on research differ substantially in how they are named, conducted and reported [ 9 , 10 ]. This creates challenges in identifying, summarizing and comparing them. In this tutorial paper, we will use the term methodological study to refer to any study that reports on the design, conduct, analysis or reporting of primary or secondary research-related reports (such as trial registry entries and conference abstracts).

In the past 10 years, there has been an increase in the use of terms related to methodological studies (based on records retrieved with a keyword search [in the title and abstract] for “methodological review” and “meta-epidemiological study” in PubMed up to December 2019), suggesting that these studies may be appearing more frequently in the literature. See Fig.  1 .

Trends in the number studies that mention “methodological review” or “meta-

epidemiological study” in PubMed.

The methods used in many methodological studies have been borrowed from systematic and scoping reviews. This practice has influenced the direction of the field, with many methodological studies including searches of electronic databases, screening of records, duplicate data extraction and assessments of risk of bias in the included studies. However, the research questions posed in methodological studies do not always require the approaches listed above, and guidance is needed on when and how to apply these methods to a methodological study. Even though methodological studies can be conducted on qualitative or mixed methods research, this paper focuses on and draws examples exclusively from quantitative research.

The objectives of this paper are to provide some insights on how to conduct methodological studies so that there is greater consistency between the research questions posed, and the design, analysis and reporting of findings. We provide multiple examples to illustrate concepts and a proposed framework for categorizing methodological studies in quantitative research.

What is a methodological study?

Any study that describes or analyzes methods (design, conduct, analysis or reporting) in published (or unpublished) literature is a methodological study. Consequently, the scope of methodological studies is quite extensive and includes, but is not limited to, topics as diverse as: research question formulation [ 11 ]; adherence to reporting guidelines [ 12 , 13 , 14 ] and consistency in reporting [ 15 ]; approaches to study analysis [ 16 ]; investigating the credibility of analyses [ 17 ]; and studies that synthesize these methodological studies [ 18 ]. While the nomenclature of methodological studies is not uniform, the intents and purposes of these studies remain fairly consistent – to describe or analyze methods in primary or secondary studies. As such, methodological studies may also be classified as a subtype of observational studies.

Parallel to this are experimental studies that compare different methods. Even though they play an important role in informing optimal research methods, experimental methodological studies are beyond the scope of this paper. Examples of such studies include the randomized trials by Buscemi et al., comparing single data extraction to double data extraction [ 19 ], and Carrasco-Labra et al., comparing approaches to presenting findings in Grading of Recommendations, Assessment, Development and Evaluations (GRADE) summary of findings tables [ 20 ]. In these studies, the unit of analysis is the person or groups of individuals applying the methods. We also direct readers to the Studies Within a Trial (SWAT) and Studies Within a Review (SWAR) programme operated through the Hub for Trials Methodology Research, for further reading as a potential useful resource for these types of experimental studies [ 21 ]. Lastly, this paper is not meant to inform the conduct of research using computational simulation and mathematical modeling for which some guidance already exists [ 22 ], or studies on the development of methods using consensus-based approaches.

When should we conduct a methodological study?

Methodological studies occupy a unique niche in health research that allows them to inform methodological advances. Methodological studies should also be conducted as pre-cursors to reporting guideline development, as they provide an opportunity to understand current practices, and help to identify the need for guidance and gaps in methodological or reporting quality. For example, the development of the popular Preferred Reporting Items of Systematic reviews and Meta-Analyses (PRISMA) guidelines were preceded by methodological studies identifying poor reporting practices [ 23 , 24 ]. In these instances, after the reporting guidelines are published, methodological studies can also be used to monitor uptake of the guidelines.

These studies can also be conducted to inform the state of the art for design, analysis and reporting practices across different types of health research fields, with the aim of improving research practices, and preventing or reducing research waste. For example, Samaan et al. conducted a scoping review of adherence to different reporting guidelines in health care literature [ 18 ]. Methodological studies can also be used to determine the factors associated with reporting practices. For example, Abbade et al. investigated journal characteristics associated with the use of the Participants, Intervention, Comparison, Outcome, Timeframe (PICOT) format in framing research questions in trials of venous ulcer disease [ 11 ].

How often are methodological studies conducted?

There is no clear answer to this question. Based on a search of PubMed, the use of related terms (“methodological review” and “meta-epidemiological study”) – and therefore, the number of methodological studies – is on the rise. However, many other terms are used to describe methodological studies. There are also many studies that explore design, conduct, analysis or reporting of research reports, but that do not use any specific terms to describe or label their study design in terms of “methodology”. This diversity in nomenclature makes a census of methodological studies elusive. Appropriate terminology and key words for methodological studies are needed to facilitate improved accessibility for end-users.

Why do we conduct methodological studies?

Methodological studies provide information on the design, conduct, analysis or reporting of primary and secondary research and can be used to appraise quality, quantity, completeness, accuracy and consistency of health research. These issues can be explored in specific fields, journals, databases, geographical regions and time periods. For example, Areia et al. explored the quality of reporting of endoscopic diagnostic studies in gastroenterology [ 25 ]; Knol et al. investigated the reporting of p -values in baseline tables in randomized trial published in high impact journals [ 26 ]; Chen et al. describe adherence to the Consolidated Standards of Reporting Trials (CONSORT) statement in Chinese Journals [ 27 ]; and Hopewell et al. describe the effect of editors’ implementation of CONSORT guidelines on reporting of abstracts over time [ 28 ]. Methodological studies provide useful information to researchers, clinicians, editors, publishers and users of health literature. As a result, these studies have been at the cornerstone of important methodological developments in the past two decades and have informed the development of many health research guidelines including the highly cited CONSORT statement [ 5 ].

Where can we find methodological studies?

Methodological studies can be found in most common biomedical bibliographic databases (e.g. Embase, MEDLINE, PubMed, Web of Science). However, the biggest caveat is that methodological studies are hard to identify in the literature due to the wide variety of names used and the lack of comprehensive databases dedicated to them. A handful can be found in the Cochrane Library as “Cochrane Methodology Reviews”, but these studies only cover methodological issues related to systematic reviews. Previous attempts to catalogue all empirical studies of methods used in reviews were abandoned 10 years ago [ 29 ]. In other databases, a variety of search terms may be applied with different levels of sensitivity and specificity.

Some frequently asked questions about methodological studies

In this section, we have outlined responses to questions that might help inform the conduct of methodological studies.

Q: How should I select research reports for my methodological study?

A: Selection of research reports for a methodological study depends on the research question and eligibility criteria. Once a clear research question is set and the nature of literature one desires to review is known, one can then begin the selection process. Selection may begin with a broad search, especially if the eligibility criteria are not apparent. For example, a methodological study of Cochrane Reviews of HIV would not require a complex search as all eligible studies can easily be retrieved from the Cochrane Library after checking a few boxes [ 30 ]. On the other hand, a methodological study of subgroup analyses in trials of gastrointestinal oncology would require a search to find such trials, and further screening to identify trials that conducted a subgroup analysis [ 31 ].

The strategies used for identifying participants in observational studies can apply here. One may use a systematic search to identify all eligible studies. If the number of eligible studies is unmanageable, a random sample of articles can be expected to provide comparable results if it is sufficiently large [ 32 ]. For example, Wilson et al. used a random sample of trials from the Cochrane Stroke Group’s Trial Register to investigate completeness of reporting [ 33 ]. It is possible that a simple random sample would lead to underrepresentation of units (i.e. research reports) that are smaller in number. This is relevant if the investigators wish to compare multiple groups but have too few units in one group. In this case a stratified sample would help to create equal groups. For example, in a methodological study comparing Cochrane and non-Cochrane reviews, Kahale et al. drew random samples from both groups [ 34 ]. Alternatively, systematic or purposeful sampling strategies can be used and we encourage researchers to justify their selected approaches based on the study objective.

Q: How many databases should I search?

A: The number of databases one should search would depend on the approach to sampling, which can include targeting the entire “population” of interest or a sample of that population. If you are interested in including the entire target population for your research question, or drawing a random or systematic sample from it, then a comprehensive and exhaustive search for relevant articles is required. In this case, we recommend using systematic approaches for searching electronic databases (i.e. at least 2 databases with a replicable and time stamped search strategy). The results of your search will constitute a sampling frame from which eligible studies can be drawn.

Alternatively, if your approach to sampling is purposeful, then we recommend targeting the database(s) or data sources (e.g. journals, registries) that include the information you need. For example, if you are conducting a methodological study of high impact journals in plastic surgery and they are all indexed in PubMed, you likely do not need to search any other databases. You may also have a comprehensive list of all journals of interest and can approach your search using the journal names in your database search (or by accessing the journal archives directly from the journal’s website). Even though one could also search journals’ web pages directly, using a database such as PubMed has multiple advantages, such as the use of filters, so the search can be narrowed down to a certain period, or study types of interest. Furthermore, individual journals’ web sites may have different search functionalities, which do not necessarily yield a consistent output.

Q: Should I publish a protocol for my methodological study?

A: A protocol is a description of intended research methods. Currently, only protocols for clinical trials require registration [ 35 ]. Protocols for systematic reviews are encouraged but no formal recommendation exists. The scientific community welcomes the publication of protocols because they help protect against selective outcome reporting, the use of post hoc methodologies to embellish results, and to help avoid duplication of efforts [ 36 ]. While the latter two risks exist in methodological research, the negative consequences may be substantially less than for clinical outcomes. In a sample of 31 methodological studies, 7 (22.6%) referenced a published protocol [ 9 ]. In the Cochrane Library, there are 15 protocols for methodological reviews (21 July 2020). This suggests that publishing protocols for methodological studies is not uncommon.

Authors can consider publishing their study protocol in a scholarly journal as a manuscript. Advantages of such publication include obtaining peer-review feedback about the planned study, and easy retrieval by searching databases such as PubMed. The disadvantages in trying to publish protocols includes delays associated with manuscript handling and peer review, as well as costs, as few journals publish study protocols, and those journals mostly charge article-processing fees [ 37 ]. Authors who would like to make their protocol publicly available without publishing it in scholarly journals, could deposit their study protocols in publicly available repositories, such as the Open Science Framework ( https://osf.io/ ).

Q: How to appraise the quality of a methodological study?

A: To date, there is no published tool for appraising the risk of bias in a methodological study, but in principle, a methodological study could be considered as a type of observational study. Therefore, during conduct or appraisal, care should be taken to avoid the biases common in observational studies [ 38 ]. These biases include selection bias, comparability of groups, and ascertainment of exposure or outcome. In other words, to generate a representative sample, a comprehensive reproducible search may be necessary to build a sampling frame. Additionally, random sampling may be necessary to ensure that all the included research reports have the same probability of being selected, and the screening and selection processes should be transparent and reproducible. To ensure that the groups compared are similar in all characteristics, matching, random sampling or stratified sampling can be used. Statistical adjustments for between-group differences can also be applied at the analysis stage. Finally, duplicate data extraction can reduce errors in assessment of exposures or outcomes.

Q: Should I justify a sample size?

A: In all instances where one is not using the target population (i.e. the group to which inferences from the research report are directed) [ 39 ], a sample size justification is good practice. The sample size justification may take the form of a description of what is expected to be achieved with the number of articles selected, or a formal sample size estimation that outlines the number of articles required to answer the research question with a certain precision and power. Sample size justifications in methodological studies are reasonable in the following instances:

Comparing two groups

Determining a proportion, mean or another quantifier

Determining factors associated with an outcome using regression-based analyses

For example, El Dib et al. computed a sample size requirement for a methodological study of diagnostic strategies in randomized trials, based on a confidence interval approach [ 40 ].

Q: What should I call my study?

A: Other terms which have been used to describe/label methodological studies include “ methodological review ”, “methodological survey” , “meta-epidemiological study” , “systematic review” , “systematic survey”, “meta-research”, “research-on-research” and many others. We recommend that the study nomenclature be clear, unambiguous, informative and allow for appropriate indexing. Methodological study nomenclature that should be avoided includes “ systematic review” – as this will likely be confused with a systematic review of a clinical question. “ Systematic survey” may also lead to confusion about whether the survey was systematic (i.e. using a preplanned methodology) or a survey using “ systematic” sampling (i.e. a sampling approach using specific intervals to determine who is selected) [ 32 ]. Any of the above meanings of the words “ systematic” may be true for methodological studies and could be potentially misleading. “ Meta-epidemiological study” is ideal for indexing, but not very informative as it describes an entire field. The term “ review ” may point towards an appraisal or “review” of the design, conduct, analysis or reporting (or methodological components) of the targeted research reports, yet it has also been used to describe narrative reviews [ 41 , 42 ]. The term “ survey ” is also in line with the approaches used in many methodological studies [ 9 ], and would be indicative of the sampling procedures of this study design. However, in the absence of guidelines on nomenclature, the term “ methodological study ” is broad enough to capture most of the scenarios of such studies.

Q: Should I account for clustering in my methodological study?

A: Data from methodological studies are often clustered. For example, articles coming from a specific source may have different reporting standards (e.g. the Cochrane Library). Articles within the same journal may be similar due to editorial practices and policies, reporting requirements and endorsement of guidelines. There is emerging evidence that these are real concerns that should be accounted for in analyses [ 43 ]. Some cluster variables are described in the section: “ What variables are relevant to methodological studies?”

A variety of modelling approaches can be used to account for correlated data, including the use of marginal, fixed or mixed effects regression models with appropriate computation of standard errors [ 44 ]. For example, Kosa et al. used generalized estimation equations to account for correlation of articles within journals [ 15 ]. Not accounting for clustering could lead to incorrect p -values, unduly narrow confidence intervals, and biased estimates [ 45 ].

Q: Should I extract data in duplicate?

A: Yes. Duplicate data extraction takes more time but results in less errors [ 19 ]. Data extraction errors in turn affect the effect estimate [ 46 ], and therefore should be mitigated. Duplicate data extraction should be considered in the absence of other approaches to minimize extraction errors. However, much like systematic reviews, this area will likely see rapid new advances with machine learning and natural language processing technologies to support researchers with screening and data extraction [ 47 , 48 ]. However, experience plays an important role in the quality of extracted data and inexperienced extractors should be paired with experienced extractors [ 46 , 49 ].

Q: Should I assess the risk of bias of research reports included in my methodological study?

A : Risk of bias is most useful in determining the certainty that can be placed in the effect measure from a study. In methodological studies, risk of bias may not serve the purpose of determining the trustworthiness of results, as effect measures are often not the primary goal of methodological studies. Determining risk of bias in methodological studies is likely a practice borrowed from systematic review methodology, but whose intrinsic value is not obvious in methodological studies. When it is part of the research question, investigators often focus on one aspect of risk of bias. For example, Speich investigated how blinding was reported in surgical trials [ 50 ], and Abraha et al., investigated the application of intention-to-treat analyses in systematic reviews and trials [ 51 ].

Q: What variables are relevant to methodological studies?

A: There is empirical evidence that certain variables may inform the findings in a methodological study. We outline some of these and provide a brief overview below:

Country: Countries and regions differ in their research cultures, and the resources available to conduct research. Therefore, it is reasonable to believe that there may be differences in methodological features across countries. Methodological studies have reported loco-regional differences in reporting quality [ 52 , 53 ]. This may also be related to challenges non-English speakers face in publishing papers in English.

Authors’ expertise: The inclusion of authors with expertise in research methodology, biostatistics, and scientific writing is likely to influence the end-product. Oltean et al. found that among randomized trials in orthopaedic surgery, the use of analyses that accounted for clustering was more likely when specialists (e.g. statistician, epidemiologist or clinical trials methodologist) were included on the study team [ 54 ]. Fleming et al. found that including methodologists in the review team was associated with appropriate use of reporting guidelines [ 55 ].

Source of funding and conflicts of interest: Some studies have found that funded studies report better [ 56 , 57 ], while others do not [ 53 , 58 ]. The presence of funding would indicate the availability of resources deployed to ensure optimal design, conduct, analysis and reporting. However, the source of funding may introduce conflicts of interest and warrant assessment. For example, Kaiser et al. investigated the effect of industry funding on obesity or nutrition randomized trials and found that reporting quality was similar [ 59 ]. Thomas et al. looked at reporting quality of long-term weight loss trials and found that industry funded studies were better [ 60 ]. Kan et al. examined the association between industry funding and “positive trials” (trials reporting a significant intervention effect) and found that industry funding was highly predictive of a positive trial [ 61 ]. This finding is similar to that of a recent Cochrane Methodology Review by Hansen et al. [ 62 ]

Journal characteristics: Certain journals’ characteristics may influence the study design, analysis or reporting. Characteristics such as journal endorsement of guidelines [ 63 , 64 ], and Journal Impact Factor (JIF) have been shown to be associated with reporting [ 63 , 65 , 66 , 67 ].

Study size (sample size/number of sites): Some studies have shown that reporting is better in larger studies [ 53 , 56 , 58 ].

Year of publication: It is reasonable to assume that design, conduct, analysis and reporting of research will change over time. Many studies have demonstrated improvements in reporting over time or after the publication of reporting guidelines [ 68 , 69 ].

Type of intervention: In a methodological study of reporting quality of weight loss intervention studies, Thabane et al. found that trials of pharmacologic interventions were reported better than trials of non-pharmacologic interventions [ 70 ].

Interactions between variables: Complex interactions between the previously listed variables are possible. High income countries with more resources may be more likely to conduct larger studies and incorporate a variety of experts. Authors in certain countries may prefer certain journals, and journal endorsement of guidelines and editorial policies may change over time.

Q: Should I focus only on high impact journals?

A: Investigators may choose to investigate only high impact journals because they are more likely to influence practice and policy, or because they assume that methodological standards would be higher. However, the JIF may severely limit the scope of articles included and may skew the sample towards articles with positive findings. The generalizability and applicability of findings from a handful of journals must be examined carefully, especially since the JIF varies over time. Even among journals that are all “high impact”, variations exist in methodological standards.

Q: Can I conduct a methodological study of qualitative research?

A: Yes. Even though a lot of methodological research has been conducted in the quantitative research field, methodological studies of qualitative studies are feasible. Certain databases that catalogue qualitative research including the Cumulative Index to Nursing & Allied Health Literature (CINAHL) have defined subject headings that are specific to methodological research (e.g. “research methodology”). Alternatively, one could also conduct a qualitative methodological review; that is, use qualitative approaches to synthesize methodological issues in qualitative studies.

Q: What reporting guidelines should I use for my methodological study?

A: There is no guideline that covers the entire scope of methodological studies. One adaptation of the PRISMA guidelines has been published, which works well for studies that aim to use the entire target population of research reports [ 71 ]. However, it is not widely used (40 citations in 2 years as of 09 December 2019), and methodological studies that are designed as cross-sectional or before-after studies require a more fit-for purpose guideline. A more encompassing reporting guideline for a broad range of methodological studies is currently under development [ 72 ]. However, in the absence of formal guidance, the requirements for scientific reporting should be respected, and authors of methodological studies should focus on transparency and reproducibility.

Q: What are the potential threats to validity and how can I avoid them?

A: Methodological studies may be compromised by a lack of internal or external validity. The main threats to internal validity in methodological studies are selection and confounding bias. Investigators must ensure that the methods used to select articles does not make them differ systematically from the set of articles to which they would like to make inferences. For example, attempting to make extrapolations to all journals after analyzing high-impact journals would be misleading.

Many factors (confounders) may distort the association between the exposure and outcome if the included research reports differ with respect to these factors [ 73 ]. For example, when examining the association between source of funding and completeness of reporting, it may be necessary to account for journals that endorse the guidelines. Confounding bias can be addressed by restriction, matching and statistical adjustment [ 73 ]. Restriction appears to be the method of choice for many investigators who choose to include only high impact journals or articles in a specific field. For example, Knol et al. examined the reporting of p -values in baseline tables of high impact journals [ 26 ]. Matching is also sometimes used. In the methodological study of non-randomized interventional studies of elective ventral hernia repair, Parker et al. matched prospective studies with retrospective studies and compared reporting standards [ 74 ]. Some other methodological studies use statistical adjustments. For example, Zhang et al. used regression techniques to determine the factors associated with missing participant data in trials [ 16 ].

With regard to external validity, researchers interested in conducting methodological studies must consider how generalizable or applicable their findings are. This should tie in closely with the research question and should be explicit. For example. Findings from methodological studies on trials published in high impact cardiology journals cannot be assumed to be applicable to trials in other fields. However, investigators must ensure that their sample truly represents the target sample either by a) conducting a comprehensive and exhaustive search, or b) using an appropriate and justified, randomly selected sample of research reports.

Even applicability to high impact journals may vary based on the investigators’ definition, and over time. For example, for high impact journals in the field of general medicine, Bouwmeester et al. included the Annals of Internal Medicine (AIM), BMJ, the Journal of the American Medical Association (JAMA), Lancet, the New England Journal of Medicine (NEJM), and PLoS Medicine ( n  = 6) [ 75 ]. In contrast, the high impact journals selected in the methodological study by Schiller et al. were BMJ, JAMA, Lancet, and NEJM ( n  = 4) [ 76 ]. Another methodological study by Kosa et al. included AIM, BMJ, JAMA, Lancet and NEJM ( n  = 5). In the methodological study by Thabut et al., journals with a JIF greater than 5 were considered to be high impact. Riado Minguez et al. used first quartile journals in the Journal Citation Reports (JCR) for a specific year to determine “high impact” [ 77 ]. Ultimately, the definition of high impact will be based on the number of journals the investigators are willing to include, the year of impact and the JIF cut-off [ 78 ]. We acknowledge that the term “generalizability” may apply differently for methodological studies, especially when in many instances it is possible to include the entire target population in the sample studied.

Finally, methodological studies are not exempt from information bias which may stem from discrepancies in the included research reports [ 79 ], errors in data extraction, or inappropriate interpretation of the information extracted. Likewise, publication bias may also be a concern in methodological studies, but such concepts have not yet been explored.

A proposed framework

In order to inform discussions about methodological studies, the development of guidance for what should be reported, we have outlined some key features of methodological studies that can be used to classify them. For each of the categories outlined below, we provide an example. In our experience, the choice of approach to completing a methodological study can be informed by asking the following four questions:

What is the aim?

Methodological studies that investigate bias

A methodological study may be focused on exploring sources of bias in primary or secondary studies (meta-bias), or how bias is analyzed. We have taken care to distinguish bias (i.e. systematic deviations from the truth irrespective of the source) from reporting quality or completeness (i.e. not adhering to a specific reporting guideline or norm). An example of where this distinction would be important is in the case of a randomized trial with no blinding. This study (depending on the nature of the intervention) would be at risk of performance bias. However, if the authors report that their study was not blinded, they would have reported adequately. In fact, some methodological studies attempt to capture both “quality of conduct” and “quality of reporting”, such as Richie et al., who reported on the risk of bias in randomized trials of pharmacy practice interventions [ 80 ]. Babic et al. investigated how risk of bias was used to inform sensitivity analyses in Cochrane reviews [ 81 ]. Further, biases related to choice of outcomes can also be explored. For example, Tan et al investigated differences in treatment effect size based on the outcome reported [ 82 ].

Methodological studies that investigate quality (or completeness) of reporting

Methodological studies may report quality of reporting against a reporting checklist (i.e. adherence to guidelines) or against expected norms. For example, Croituro et al. report on the quality of reporting in systematic reviews published in dermatology journals based on their adherence to the PRISMA statement [ 83 ], and Khan et al. described the quality of reporting of harms in randomized controlled trials published in high impact cardiovascular journals based on the CONSORT extension for harms [ 84 ]. Other methodological studies investigate reporting of certain features of interest that may not be part of formally published checklists or guidelines. For example, Mbuagbaw et al. described how often the implications for research are elaborated using the Evidence, Participants, Intervention, Comparison, Outcome, Timeframe (EPICOT) format [ 30 ].

Methodological studies that investigate the consistency of reporting

Sometimes investigators may be interested in how consistent reports of the same research are, as it is expected that there should be consistency between: conference abstracts and published manuscripts; manuscript abstracts and manuscript main text; and trial registration and published manuscript. For example, Rosmarakis et al. investigated consistency between conference abstracts and full text manuscripts [ 85 ].

Methodological studies that investigate factors associated with reporting

In addition to identifying issues with reporting in primary and secondary studies, authors of methodological studies may be interested in determining the factors that are associated with certain reporting practices. Many methodological studies incorporate this, albeit as a secondary outcome. For example, Farrokhyar et al. investigated the factors associated with reporting quality in randomized trials of coronary artery bypass grafting surgery [ 53 ].

Methodological studies that investigate methods

Methodological studies may also be used to describe methods or compare methods, and the factors associated with methods. Muller et al. described the methods used for systematic reviews and meta-analyses of observational studies [ 86 ].

Methodological studies that summarize other methodological studies

Some methodological studies synthesize results from other methodological studies. For example, Li et al. conducted a scoping review of methodological reviews that investigated consistency between full text and abstracts in primary biomedical research [ 87 ].

Methodological studies that investigate nomenclature and terminology

Some methodological studies may investigate the use of names and terms in health research. For example, Martinic et al. investigated the definitions of systematic reviews used in overviews of systematic reviews (OSRs), meta-epidemiological studies and epidemiology textbooks [ 88 ].

Other types of methodological studies

In addition to the previously mentioned experimental methodological studies, there may exist other types of methodological studies not captured here.

What is the design?

Methodological studies that are descriptive

Most methodological studies are purely descriptive and report their findings as counts (percent) and means (standard deviation) or medians (interquartile range). For example, Mbuagbaw et al. described the reporting of research recommendations in Cochrane HIV systematic reviews [ 30 ]. Gohari et al. described the quality of reporting of randomized trials in diabetes in Iran [ 12 ].

Methodological studies that are analytical

Some methodological studies are analytical wherein “analytical studies identify and quantify associations, test hypotheses, identify causes and determine whether an association exists between variables, such as between an exposure and a disease.” [ 89 ] In the case of methodological studies all these investigations are possible. For example, Kosa et al. investigated the association between agreement in primary outcome from trial registry to published manuscript and study covariates. They found that larger and more recent studies were more likely to have agreement [ 15 ]. Tricco et al. compared the conclusion statements from Cochrane and non-Cochrane systematic reviews with a meta-analysis of the primary outcome and found that non-Cochrane reviews were more likely to report positive findings. These results are a test of the null hypothesis that the proportions of Cochrane and non-Cochrane reviews that report positive results are equal [ 90 ].

What is the sampling strategy?

Methodological studies that include the target population

Methodological reviews with narrow research questions may be able to include the entire target population. For example, in the methodological study of Cochrane HIV systematic reviews, Mbuagbaw et al. included all of the available studies ( n  = 103) [ 30 ].

Methodological studies that include a sample of the target population

Many methodological studies use random samples of the target population [ 33 , 91 , 92 ]. Alternatively, purposeful sampling may be used, limiting the sample to a subset of research-related reports published within a certain time period, or in journals with a certain ranking or on a topic. Systematic sampling can also be used when random sampling may be challenging to implement.

What is the unit of analysis?

Methodological studies with a research report as the unit of analysis

Many methodological studies use a research report (e.g. full manuscript of study, abstract portion of the study) as the unit of analysis, and inferences can be made at the study-level. However, both published and unpublished research-related reports can be studied. These may include articles, conference abstracts, registry entries etc.

Methodological studies with a design, analysis or reporting item as the unit of analysis

Some methodological studies report on items which may occur more than once per article. For example, Paquette et al. report on subgroup analyses in Cochrane reviews of atrial fibrillation in which 17 systematic reviews planned 56 subgroup analyses [ 93 ].

This framework is outlined in Fig.  2 .

A proposed framework for methodological studies

Conclusions

Methodological studies have examined different aspects of reporting such as quality, completeness, consistency and adherence to reporting guidelines. As such, many of the methodological study examples cited in this tutorial are related to reporting. However, as an evolving field, the scope of research questions that can be addressed by methodological studies is expected to increase.

In this paper we have outlined the scope and purpose of methodological studies, along with examples of instances in which various approaches have been used. In the absence of formal guidance on the design, conduct, analysis and reporting of methodological studies, we have provided some advice to help make methodological studies consistent. This advice is grounded in good contemporary scientific practice. Generally, the research question should tie in with the sampling approach and planned analysis. We have also highlighted the variables that may inform findings from methodological studies. Lastly, we have provided suggestions for ways in which authors can categorize their methodological studies to inform their design and analysis.

Availability of data and materials

Data sharing is not applicable to this article as no new data were created or analyzed in this study.

Abbreviations

Consolidated Standards of Reporting Trials

Evidence, Participants, Intervention, Comparison, Outcome, Timeframe

Grading of Recommendations, Assessment, Development and Evaluations

Participants, Intervention, Comparison, Outcome, Timeframe

Preferred Reporting Items of Systematic reviews and Meta-Analyses

Studies Within a Review

Studies Within a Trial

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• What Is Qualitative Research? | Methods & Examples

What Is Qualitative Research? | Methods & Examples

Published on June 19, 2020 by Pritha Bhandari . Revised on June 22, 2023.

Qualitative research involves collecting and analyzing non-numerical data (e.g., text, video, or audio) to understand concepts, opinions, or experiences. It can be used to gather in-depth insights into a problem or generate new ideas for research.

Qualitative research is the opposite of quantitative research , which involves collecting and analyzing numerical data for statistical analysis.

Qualitative research is commonly used in the humanities and social sciences, in subjects such as anthropology, sociology, education, health sciences, history, etc.

• How does social media shape body image in teenagers?
• How do children and adults interpret healthy eating in the UK?
• What factors influence employee retention in a large organization?
• How is anxiety experienced around the world?
• How can teachers integrate social issues into science curriculums?

Table of contents

Approaches to qualitative research, qualitative research methods, qualitative data analysis, advantages of qualitative research, disadvantages of qualitative research, other interesting articles, frequently asked questions about qualitative research.

Qualitative research is used to understand how people experience the world. While there are many approaches to qualitative research, they tend to be flexible and focus on retaining rich meaning when interpreting data.

Common approaches include grounded theory, ethnography , action research , phenomenological research, and narrative research. They share some similarities, but emphasize different aims and perspectives.

Note that qualitative research is at risk for certain research biases including the Hawthorne effect , observer bias , recall bias , and social desirability bias . While not always totally avoidable, awareness of potential biases as you collect and analyze your data can prevent them from impacting your work too much.

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Each of the research approaches involve using one or more data collection methods . These are some of the most common qualitative methods:

• Observations: recording what you have seen, heard, or encountered in detailed field notes.
• Interviews:  personally asking people questions in one-on-one conversations.
• Focus groups: asking questions and generating discussion among a group of people.
• Surveys : distributing questionnaires with open-ended questions.
• Secondary research: collecting existing data in the form of texts, images, audio or video recordings, etc.
• You take field notes with observations and reflect on your own experiences of the company culture.
• You distribute open-ended surveys to employees across all the company’s offices by email to find out if the culture varies across locations.
• You conduct in-depth interviews with employees in your office to learn about their experiences and perspectives in greater detail.

Qualitative researchers often consider themselves “instruments” in research because all observations, interpretations and analyses are filtered through their own personal lens.

For this reason, when writing up your methodology for qualitative research, it’s important to reflect on your approach and to thoroughly explain the choices you made in collecting and analyzing the data.

Qualitative data can take the form of texts, photos, videos and audio. For example, you might be working with interview transcripts, survey responses, fieldnotes, or recordings from natural settings.

Most types of qualitative data analysis share the same five steps:

• Prepare and organize your data. This may mean transcribing interviews or typing up fieldnotes.
• Review and explore your data. Examine the data for patterns or repeated ideas that emerge.
• Develop a data coding system. Based on your initial ideas, establish a set of codes that you can apply to categorize your data.
• Assign codes to the data. For example, in qualitative survey analysis, this may mean going through each participant’s responses and tagging them with codes in a spreadsheet. As you go through your data, you can create new codes to add to your system if necessary.
• Identify recurring themes. Link codes together into cohesive, overarching themes.

There are several specific approaches to analyzing qualitative data. Although these methods share similar processes, they emphasize different concepts.

Qualitative research often tries to preserve the voice and perspective of participants and can be adjusted as new research questions arise. Qualitative research is good for:

• Flexibility

The data collection and analysis process can be adapted as new ideas or patterns emerge. They are not rigidly decided beforehand.

• Natural settings

Data collection occurs in real-world contexts or in naturalistic ways.

• Meaningful insights

Detailed descriptions of people’s experiences, feelings and perceptions can be used in designing, testing or improving systems or products.

• Generation of new ideas

Open-ended responses mean that researchers can uncover novel problems or opportunities that they wouldn’t have thought of otherwise.

Researchers must consider practical and theoretical limitations in analyzing and interpreting their data. Qualitative research suffers from:

• Unreliability

The real-world setting often makes qualitative research unreliable because of uncontrolled factors that affect the data.

• Subjectivity

Due to the researcher’s primary role in analyzing and interpreting data, qualitative research cannot be replicated . The researcher decides what is important and what is irrelevant in data analysis, so interpretations of the same data can vary greatly.

• Limited generalizability

Small samples are often used to gather detailed data about specific contexts. Despite rigorous analysis procedures, it is difficult to draw generalizable conclusions because the data may be biased and unrepresentative of the wider population .

• Labor-intensive

Although software can be used to manage and record large amounts of text, data analysis often has to be checked or performed manually.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Chi square goodness of fit test
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Inclusion and exclusion criteria

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

There are five common approaches to qualitative research :

• Grounded theory involves collecting data in order to develop new theories.
• Ethnography involves immersing yourself in a group or organization to understand its culture.
• Narrative research involves interpreting stories to understand how people make sense of their experiences and perceptions.
• Phenomenological research involves investigating phenomena through people’s lived experiences.
• Action research links theory and practice in several cycles to drive innovative changes.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organize your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

Cite this Scribbr article

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• Published: 06 September 2024

ST-GEARS: Advancing 3D downstream research through accurate spatial information recovery

• Tianyi Xia 1 , 2 ,
• Luni Hu 1 , 2 ,
• Lulu Zuo 3 ,
• Lei Cao 1 , 2 ,
• Yunjia Zhang 1 , 2 ,
• Mengyang Xu   ORCID: orcid.org/0000-0002-4487-7088 2 , 4 ,
• Lei Zhang 1 , 2 ,
• Taotao Pan 1 , 2 ,
• Bohan Zhang   ORCID: orcid.org/0000-0001-6654-3567 1 , 2 ,
• Bowen Ma 1 , 2 ,
• Chuan Chen 1 , 2 ,
• Junfu Guo   ORCID: orcid.org/0000-0002-4195-7031 3 ,
• Chang Shi 3 ,
• Mei Li   ORCID: orcid.org/0000-0003-3310-2911 2 ,
• Chao Liu   ORCID: orcid.org/0009-0008-6892-6754 1 , 2 ,
• Yuxiang Li   ORCID: orcid.org/0000-0002-1575-3692 2 , 5 , 6 ,
• Yong Zhang   ORCID: orcid.org/0000-0001-9950-1793 2 , 5 , 6 &
• Shuangsang Fang   ORCID: orcid.org/0000-0002-4126-0074 1 , 2  

Nature Communications volume  15 , Article number:  7806 ( 2024 ) Cite this article

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• Transcriptomics

Three-dimensional Spatial Transcriptomics has revolutionized our understanding of tissue regionalization, organogenesis, and development. However, existing approaches overlook either spatial information or experiment-induced distortions, leading to significant discrepancies between reconstruction results and in vivo cell locations, causing unreliable downstream analysis. To address these challenges, we propose ST-GEARS (Spatial Transcriptomics GEospatial profile recovery system through AnchoRS). By employing innovative Distributive Constraints into the Optimization scheme, ST-GEARS retrieves anchors with exceeding precision that connect closest spots across sections in vivo. Guided by the anchors, it first rigidly aligns sections, next solves and denoises Elastic Fields to counteract distortions. Through mathematically proved Bi-sectional Fields Application, it eventually recovers the original spatial profile. Studying ST-GEARS across number of sections, sectional distances and sequencing platforms, we observed its outstanding performance on tissue, cell, and gene levels. ST-GEARS provides precise and well-explainable ‘gears’ between in vivo situations and in vitro analysis, powerfully fueling potential of biological discoveries.

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Alignment and integration of spatial transcriptomics data

SANTO: a coarse-to-fine alignment and stitching method for spatial omics

NovoSpaRc: flexible spatial reconstruction of single-cell gene expression with optimal transport

Introduction.

Spatial transcriptomics (ST) is an omics technology that fuels biological research based on measuring gene expression on each position-recorded spot across sliced tissues 1 , 2 , 3 . Notably, a range of methods has been developed. In vivo sequencing (ISS) 4 platforms such as Barcoded Anatomy Resolved by Sequencing (BARseq) 5 and Spatially-resolved Transcript Amplicon Readout Mapping (STARmap) 6 rely on amplification, hybridization and imaging process to capture gene expression information. Next Generation Sequencing (NGS) 7 platform such as Visium 1 , Stereo-seq 8 and Slide-Seq2 9 uses spatial barcoding and capturing in their implementations. These methods offer various sequencing resolutions ranging from 100 µm 10 , 11 to 500 nm 8 , and can measure thousands 5 to tens of thousands 8 of genes simultaneously.

Single-slice ST studies have unleashed discoveries, and facilitated our understanding in diverse biological and medical fields 9 , 12 , 13 , 14 , 15 . Consequently, numerous processing pipelines and analysis models have been developed for ST data on a single section 16 , 17 , 18 , 19 , 20 , 21 . However, to truly capture transcriptomics in the real-world context, three-dimensional (3D) ST was designed to recover biological states and processes in real-world dimensions, without restriction of the isolated planes in single sectional ST studies. Various research has utilized the power of 3D ST to uncover insights in homeostasis, development, and diseases. Among them, Wang et al. 22 uncovered spatial cell state dynamics of Drosophila larval testis and revealed potential regulons of transcription factors. Mohenska et al. 23 revealed complex spatial patterns in Murine heart and identified novel markers for cardiac subsections. And Vickovic et al. 24 explored cell type localizations in Human rheumatoid arthritis synovium. The vast and large variety of downstream 3D research has posted the need for a reliable and automatic recovery method of in vivo spatial profile.

However, the collection process of ST data casts significant challenges onto the accurate reconstruction of 3D ST and the situation has not been overcome by current explorations. Specifically, in 3D ST experiments, individual slices are cross sectioned in a consistent direction, then manually placed on different chips or slides 14 , 25 . This operation introduces varying geospatial reference systems of distinct sections, and coordinates are distorted compared to their in vivo states. The distortions occur due to squeezing and stretching effects during the picking, holding, and relocation of the sections. Such different geospatial systems and distortions complicates the recovery of in vivo 3D profile. Among current recovery approaches, STUtility 26 realizes multi-section alignment through the registration of histology images, without considering either geospatial or molecular profile of mRNA, which leads to compromised accuracies. Recently published method PASTE 27 , and its second version PASTE2 28 achieve alignment using both gene expression and coordinate information, through optimization of mapping between individual spots across sections. These methods cause inaccurate mappings and produces rotational misalignments due to the nonadaptive regularization factors, and their uniform sum of probability assigned to all spots upon presence of spots without actual anchors. All above approaches only consider rigid alignment, yet neglect the correction of shape distortions, resulting in shape inconsistency across registered sections. Published method Gaussian Process Spatial Alignment (GPSA) 29 considers shape distortions in its alignment, yet it doesn’t involve structural consistency in its loss function, which can cause the model to overfit to local gene expression similarities, leading to mistaken distortions of spatial information. Moreover, its hypothesis space involves readout prediction in addition to coordinates alignment, causing uncertainty in direction of gradient descent, and vulnerabilities to input noises. Another alignment approach, Spatial-linked alignment tool (SLAT) 30 also focuses on anchors construction between sections, yet it doesn’t provide a methodology to construct 3D transcriptomics profile. Other tools focus on analysis and visualization of 3d data, such as Spateo 31 , VT3D 32 and StereoPy 33 .

To address these limitations, we introduce ST-GEARS, a 3D geospatial profile recovery approach designed for ST experiments. By formulating the problem using the framework of Fused Gromov-Wasserstein (FGW) Optimal Transport (OT) 34 , ST-GEARS incorporates both gene expression and structural similarity into the Optimization process to retrieve cross-sectional mappings of spots with the same in vivo planar positions, also referred to as ‘anchors’. During this process, we introduce innovative Distributive Constraints that allow for different emphasis on distinct spot groups. The strategy addresses importance of expression consistent groups and suppresses inconsistent groups from imposing disturbances to optimization. Hence it increases anchor accuracy compared to current approaches. ST-GEARS utilizes the retrieved anchors to initially perform rigid alignment of sections. Subsequently, it introduces Elastic Field guided by the anchors to represent the deformation and knowledge to correct it according to each spot’s location. To enhance the quality of the field, Gaussian Smoothing is applied for denoising purposes. ST-GEARS then applies Bi-sectional Application to correction of each section’s spatial profile based on its denoised fields calculated with its neighboring sections. With validity proved mathematically, Bi-sectional Application eliminates distortions of sections, resulting in the successful recovery of a 3D in vivo spatial profile.

To understand effects of ST-GEARS, we first studied its counterparts with innovations including anchors retrieval and elastic registration, respectively on Human dorsolateral prefrontal cortex (DLPFC) 35 , and Drosophila larva 22 . We found an advanced anchors accuracy of ST-GEARS compared to other available methods involving anchor’s concept and unveiled Distributive Constraints as reason behind the advancement. We validated the effectiveness of elastic registration process of ST-GEARS on both tissue shape smoothness and cross-sectional consistency. Then, we studied output of ST-GEARS and other methods on their reconstruction of Mouse hippocampus tissues 36 , Drosophila embryo individual 22 and a complete Mouse brain 37 . The result was studied on morphological, cell and gene levels. ST-GEARS was found to be the only method that correctly reconstruct on all cases despite of cross-sectioning distance, number of sections, and sequencing platforms, and it was found to output the most accurate spatial information under both annotation type or clustering information, and hybridization evidence.

ST-GEARS algorithm

ST-GEARS uses ST data as its inputs, including mRNA expression, spatial coordinates as well as approximate grouping information such as clustering or annotation of each observation. Then it recovers 3D geospatial profile in following steps (Fig.  1 ).

a The automatic pipeline of ST-GEARS which recovers ST-GEARS 3D in vivo spatial information by ordered steps including Fused Gromov Wasserstein (FGW) Optimal Transport (OT) problem parameter computing, problem formulating and solving which outputs probabilistic anchors across sections, rigid registration through Procrustes Analysis which solves optimal positional alignment using the anchors, and finally elastic registration. The input of the method is Unique molecular identifier (UMI) counts and location of each spot measured by ST technology, along with their annotations or cross-section clustering result. And the output of the method is recovered 3D in vivo spatial information of the experimented tissue, or sample. b FGW OT problem parameter computing, which assigns nonuniform weights to spots in preparation for future problem formulating, based on cross-sectional similarity of annotation types or clusters. c FGW OT problem formulating, whose setting aims to solve probabilistic anchors joining spots with highest in vivo proximity, through optimizing the combination of gene expression and structural similarity 34 . FGW OT problem solving, which is implemented based on Conditional Gradient (CG) method, leading to retrieved probabilistic anchors. d Elastic registration, which utilizes the anchors again to compute and denoise distortion fields which guides the elimination of distortions, then applies the fields bi-sectionally to positionally aligned sections, leading to the recovered 3D in vivo spatial information.

(1) Optimization problem formulation under scheme of FGW OT with enhancement of Distributive Constraints. FGW OT formulation is established to enable solving of ‘anchors’, which are the joining of pair of spots with same in vivo planar positions. Noticeably, each solved anchor is equipped with a probability that describes its strength of connection, and each spot is solved to have zero to multiple anchors. Among each two sections, section-specific groups of spots, and genes are initially excluded from the formulation to avoid causing disturbances to anchors computing. Considering that connected spots are more spatially approximate, and more similar in gene expression because of shared cell identity 38 , 39 , FGW was adopted to combine the gene expression and structural terms in optimization, enabling highest gene expression similarity between mapped spots, at the same time keeping similar spot positions relative to their sections. Moreover, an innovative Distributive Constraints setting is designed and integrated into FGW OT’s formulation, to assign higher emphasis on spots or cells whose annotation or cluster express high similarity across section, and vice versa. Distributive Constraints leads registration to rely more on expression-consistent regions of sections, hence largely enhancing both accuracy of anchors and precision of following rigid and elastic registration.

(2) Optimization problem solving utilizing self-adaptive regularization and conditional gradient descent. Our designed Self-adaptive Regularization strategy automatically determines the relative importance between gene expression and structural terms in the optimization problem. This strategy leads to an optimal regularization factor across different section distances, spot sizes, extent of distortions, and data quality such as level of diffusion. Conditional Gradient 34 is adopted as optimizer, which updates anchors iteratively towards higher expression and structural similarity with each iteration. The efficacy of Conditional Gradient has been demonstrated through its convergence to a local optimal point 40 , thereby ensuring the robustness and effectiveness of our approach.

(3) Rigid registration by Procrustes Analysis 41 . After filtering out anchors with relatively low probabilities, the optimal transformation and rotation of each section are analytically solved through Procrustes Analysis, which minimizes summed spatial distances of spots anchored to each other. With the transformation and rotation applied, sections are positionally aligned.

(4) Elastic registration guided by anchors. Based on rigid registration result and anchors solved by FGW OT, elastic registration is implemented through the process including elastic field inference, 2D Gaussian denoising, and bi-sectional fields application. Based on each rigidly registered section, elastic fields is inferred leveraging the location difference between its own spots and its anchored spots on anterior and posterior neighbor sections. An elastic field is a 2D displacement distribution, describing how displacement values are distributed across different locations. Making use of continuity of deformation at local scales, 2D Gaussian Denoising convolutes all over the fields to reduce noises. With denoised fields, our designed Bi-sectional Fields Application corrects each section’s deformation according to its fields calculated with anterior and posterior neighbor sections. The bi-sectional correction method is mathematically proved to approximately recover each section’s spatial profile to its original state.

Enhancement of anchor retrieval accuracy through distributive constraints

As was unfolded, ST-GEARS is an algorithm flow jointly constituted of probabilistic anchor computation and spatial information recovery. Hence, to validate the effectiveness of our method and demonstrate its underlying design philosophy, we conducted comprehensive studies on the two counterparts using real-world data. To begin, we utilized the DLPFC dataset 35 to study our anchors retrieving accuracy with emphasis on the effect of Distributive Constraints design.

To assess the effects of Distributive Constraints on anchor accuracy, we compared ST-GEARS with and without this setting, and with other constraints involving methods including PASTE, PASTE2 and SLAT. We investigated constraint values assigned by these methods, as well as their solved number of anchors and maximum anchor probability of each spot. Furthermore, we examined the annotation types that were considered connected based on the computed anchors to assess accuracy of anchors. Among the methods we compared, ST-GEARS with Distributive Constraints was found to assign different constraint values to spots within different neuron layers, while the others assigned uniform constraints to all layers (Fig.  2a , Supplementary Fig.  1 ). The results of ST-GEARS showed that both number of anchors and the anchors’ maximum probabilities for each spot were lower in Layer 2 and Layer 4 compared to the thicker layers. However, this pattern was not observed in methods without Distributive Constraints setting (Fig.  2a , Supplementary Fig.  1 ). To illustrate the impact of this strategy on anchor accuracy, we tagged each spot with annotation of its connected spot by anchor with highest probability. We then compared this result to the tagged spot’s original annotation (Fig.  2a , Supplementary Fig.  1 ). Under Distributive Constraints, ST-GEARS achieved a significantly higher proximity between annotations compared to PASTE and our method without Distributive Constraints. PASTE2 also led to approximate annotations, but it anchored multiple spots to spots from neighboring layers, particularly those near layer boundaries. SLAT also mapped multiple spots to spots from different tissue layers, particularly of spots located on layer 2, 4 and 6.

a (from left to right) 1st and 2nd human dorsolateral prefrontal cortex (DLPFC) section of patient #3 by Maynard et al. 35 with their provided annotations and our anchors showcase, (of the same section pair) probabilistic constraints settings in Optimal Transport (OT) problem formulating, no. of anchors computed on each spot, max. anchor probability value computed of each spot, and annotation type mapped back to spots through computed anchors; (from top to bottom) respectively by PASTE, PASTE2, SLAT, ours without distributive constraints setting, and ours. The distinction of different annotation types on the 1st section is marked by dotted lines. Mapping accuracy is used to measure accuracy of anchors and is marked alongside respective annotation type mapping visualizations. b Mapping accuracy measured on anchors of sections pairs used in ( b ) by PASTE, PASTE2, SLAT, and ST-GEARS. c Comparison of no. of anchors histograms between ST-GEARS and ST-GEARS without distributive constraints, of sections pairs of 1st and 2nd, 2nd and 3rd, and 3rd and 4th sections. The Probability Density Function (PDF) estimated by Gaussian kernel was plotted in dotted lines with the same color of histograms, to highlight the distribution differences. Source data are provided as a Source Data file.

To evaluate the precision of anchors, we conducted a comparison with the Mapping accuracy index introduced by PASTE 27 . This index measures the weighted percentage \({\sum}_{i,j,l(i)=l(j)}{\pi }_{{ij}}\) of anchors that connect spots with same annotation. As a result, ST-GEARS outperformed PASTE2 and SLAT, and reached a score that was over 0.5 (out of 1) higher than both PASTE and our method without Distributive Constraints (Fig.  2a , b , Supplementary Fig.  1 ).

To uncover the reasons behind the aforementioned phenomena, as the functional area in between thicker neocortical layers, thinner neocortical layers have comparable transcriptomic similarity with their adjacent layers in gene expression, than with its own annotation type 1 , 35 . This implies that, in contrast to thicker layers, thinner layers tend to introduce more disturbances during anchor computation. However, the Distributive Constraints imposed suppression on these annotation types by assigning a smaller sum of probability to each of their spots. The suppression was reflected in above results where each spot in Layer 2 and Layer 4 has fewer assigned anchors and a lower maximum probability (Fig.  2a , Supplementary Fig.  1 ). Further analysis on all spots in the DLPFC reveals that a certain percentage of spots were suppressed in anchor generation due to the Distributive Constraints (Fig.  2c , Supplementary Fig.  2 ).

Recovery of in vivo shape profile through elastic registration

We then utilized Drosophila larva data to investigate the spatial profile recovery effect of ST-GEARS, with an emphasis on our innovated elastic registration. We first applied rigid registration to Drosophila larva sections and observed a visually aligned configuration of individual sections (Supplementary Fig.  3 ). By further mapping cell annotations back to their previous sections, according to the strongest anchors of each spot, the projected annotations are visually in match with original ones (Supplementary Fig.  4 ). The accuracy of the mapping matching between annotations was quantified by Mapping accuracy (Supplementary Fig.  5 ). The above findings validated that ST-GEARS produced reliable anchors and accurately aligned sections through rigid registration. However, when stacking the sections together, we observed an inconsistency on the edge of lateral cross-section of the rigid result (Supplementary Fig.  6 ). This inconsistency doesn’t conform to the knowledge of intra-tissue and overall structural continuity of Drosophila larvae.

After applying elastic registration to the rigidly-aligned larva, we observed a notable improvement in the continuity of the cross section above, indicating a closer-to-real spatial information being retrieved. To further understand the effect of elastic operation on the dataset, we compared the changes in area of the complete body and three individual tissues (trachea, central nervous system (CNS), and fat body) on all sections. We observed an enhanced smoothness in the curves of elastically registered sections, which aligns with the continuous morphology of the larva as expected by theoretical knowledge. To quantify the smoothing effect, we calculated Scale-independent Standard Deviation of Differences ( \({SI}-{STD}-{DI}={STD}(\{{s}_{i}-{s}_{i-1}:i\in [{\mathrm{1,2}},...,I-1]\})/{|mean}(\{{s}_{i}-{s}_{i-1}:i\in [{\mathrm{1,2}},...,I-1]\})|\) ) onto the curves, which measures the smoothness of area changes along the sectioning direction (Fig.  3a and Methods). A decrease of SI-STD-DI on all tissues and the body provided empirical evidence for the improved smoothness. To further investigate the recovery of internal structures, we introduced Mean Structural Similarity (MSSIM). MSSIM takes structurally consistent sections as input, and measures pairwise internal similarity of reconstructed result using annotations or clustering information (Supplementary Fig.  7 ). (See Methods for details). An improved MSSIM was noticed on all 4 sections, indicating that elastic registration further recovers internal geospatial continuity on basis of rigid operation(Fig.  3b ). By comparing registration effect of individual sections, we also observed that the elastic process successfully rectified a bending flaw along the edge of the third section, (Fig.  3c ). The shape fixing highlighted that ST-GEARS not only yielded a more structurally consistent 3D volume, but also provided a more accurate morphology for single sections. The improved smoothness, the recovered structural continuity, and the shape fixing collectively demonstrate that elastic registration effectively recovers geospatial profile.

a A comparison of area changes of 3 tissues and complete body of Drosophila Larva, between result of rigid registration and result of elastic registration appended to rigid registration. The areas are calculated based on recovered spot position of different tissues along cross-sectioning direction. Standard Deviation of Differences (SI-STD-DI) quantifying the smoothness is marked alongside each curve. b A comparison of structural accuracy, measured by Mean Structural Similarity (MSSIM), of selected section pairs from Drosophila Larva (L3), between result of rigid registration only and result of elastic registration appended to rigid registration. The chosen section pairs are the structurally consistent ones. c Comparison of individual sections recovered by rigid registration only and by elastic registration appended to rigid registration, of 1st to 5th section of Drosophila Larva (L3). Shape correction of bended area in the 3 rd section, and increased cross-sectional consistency on the 4th and 5th section were highlighted by blue arrows. Source data are provided as a Source Data file.

With elastic process validated and applied onto rigid registration result, the recovery of spatial information was completed. Stacking individual sections of the elastic result, a complete geospatial profile of the larva was generated (Supplementary Fig.  8 ), visualizing the ST-GEARS’ ability of in vivo spatial information recovery.

Application to sagittal sections of Mouse hippocampus

After validating the component phases of ST-GEARS, we proceeded to apply the method to multiple real-world problems to recover geospatial profiles. We first focused on two sagittal sections of Mouse hippocampus 36 (Supplementary Fig.  9 ) that were 10 μm apart, accounting for 1–2 layers of Cornu Ammonis (CA) 1 neurons 42 . Considering the proximity of these sections, we assumed no structural differences between them.

To compare the differences of registration effect among methods, we extracted CA fields and dentate gyrus (DG) beads (Supplementary Fig.  10 ), then stacked the two sections for a more obvious contrast (Fig.  4a ). PASTE2 failed in performing the registration, leaving the sections unaligned. By GPSA, the sections’ positions were aligned, yet the 2nd section were squeezed into a narrower region than first one, leading to a contradiction of region’s location. The ‘narrowing’ phenomena may be caused by the overfitting of GPSA model on gene expression similarity, since it doesn’t involve structural similarity between registered sections in loss function. The scale on horizontal and vertical axis was distorted due to the equal scale range strategy adopted in GPSA’s preprocessing. STalign also misaligned the sections, leaving an obvious angle between two slices in registration result. This may be due to the method’s processing of ST data into images which completely relies on gene expression abundance to decide pixel intensities. On the sagittal section of Mouse hippocampus, the abundance difference between regions may not provide sufficient structural information required by registration. In the comparison between PASTE and ST-GEARS, our method demonstrates a more accurate centerline overlapping of CA fields and DG compared to PASTE. This indicated an enhanced recovery of spatial structure consistency and an improved registration effect. To quantitatively evaluate these findings, we utilized the MSSIM index as a measure of structural consistency and compared it among PASTE, PASTE2, GPSA, STalign and ST-GEARS (Fig.  4b ). Consistent with the results of centerline, ST-GEARS achieved a higher MSSIM score than GPSA and PASTE, surpassing PASTE2 and STalign by >0.2 out of 1. By comparing memory efficiency across all methods, ST-GEARS and PASTE used ~1 GB less memory than PASTE2, GPSA and STalign, and the peak memory across ST-GEARS and PASTE was almost the same (Supplementary Fig.  11 ). In perspective of time efficiency, registration utilizing ST-GEARS, STalign, GPSA and PASTE was much faster than PASTE2.

a Stacked projections of Cornu Ammonis (CA) fields and dentate gyrus (DG), of pre-registered and registered result of Mouse hippocampus sagittal sections with 10 µm distance, respectively by PASTE, PASTE2, GPSA, STalign and ST-GEARS. b A comparison of both MSSIM measuring structural accuracy and Mapping accuracy measuring anchor accuracy of the 2 registered sections, across PASTE, PASTE2, GPSA, STalign and ST-GEARS. c Stacked projections of region-specific annotation types including DG, Neurogenesis, subiculum, CA1, CA2 and CA3, registered by ST-GEARS. Each column highlights the stacked projection of a single annotation type. Source data are provided as a Source Data file.

To understand reasons behind our enhancement, we thoroughly examined the anchors generated by PASTE, PASTE2 and ST-GEARS, as well as the effects of our elastic registration. By mapping cluster information of the 2nd section to the 1st, and the 1st to the 2nd through anchors, we found correspondences between the projected and original annotations (Supplementary Fig.  12 ). Accordingly, our Mapping accuracy was over 0.25 higher than PASTE and over 0.45 than PASTE2 (Fig.  4a ), indicating our exceptional anchor accuracy. To understand and further substantiate this advantage, we visualized the probabilistic constraints and its resulted anchors probabilities (Supplementary Fig.  13a ). It is worth noting that ST-GEARS implemented Distributive Constraints, in contrast to the uniform distributions used by PASTE. As a result, a certain percentage of spots were found to be suppressed in anchors connection by ST-GEARS (Supplementary Fig.  13b ) compared to PASTE, leaving the registration to rely more on spots with higher cross-sectional similarity and less computational disturbances, and hence lead to a higher anchor accuracy. We excluded Distributive Constraints from ST-GEARS, and noticed an obvious decrease of mapping accuracy on the hippocampus dataset (Supplementary Fig.  14 ), indicating the contribution of Distributive Constraints on anchors accuracy. In the study of elastic effect, we found an increased overlapping of centerlines by elastic registration than by rigid operation only when overlapping CA fields and DG (Fig.  4b ). Quantitively by MSSIM, the cross-sectional similarity was found to be increased by elastic registration (Supplementary Fig.  15 ). These findings suggest that the combination of Distributive Constraints and elastic process contributed to the enhanced registration of the Mouse hippocampus.

To explore the potential effect of impact of our registration on downstream analysis, we extracted region-specific annotation types from the sections, and analyzed their overlapping through stacking registered sections together (Fig.  4c ). In all annotation types including DG, Neurogenesis, subiculum, CA1, CA2 and CA3, the distribution regions from both sections were nearly identical. The overlapping result unveils that ST-GEARS integrated the spatial profile of same cell subpopulations, enabling a convenient and accurate downstream analysis of multiple sections.

Application to 3D reconstruction of Drosophila embryo

Besides tissue level registration of Mouse hippocampus, to evaluate the performance of ST-GEARS in reconstructing individual with multiple sections, we further tested it on a Drosophila embryo. The transcriptomics of embryo was measured by Stereo-seq, with 7 μm cross-sectioning distance 22 . By quantifying the registration effect of spatial information recovery and comparing it to PASTE, PASTE2, GPSA and STalign, we found that ST-GEARS achieved the highest MSSIM in five out of the six structurally consistent pairs (Fig.  5a ). On the pair where ST-GEARS did not result in highest MSSIM, it surpassed PASTE, and achieved a similar score to PASTE2. By comparing area changes with SI-STD-DI quantification of the complete section, and three individual tissues including epidermis, midgut and foregut, ST-GEARS yielded higher smoothness on all regions than all other approaches, both visually and quantitatively (Fig.  5b ).

a A comparison of Mean Structural Similarity (MSSIM) measuring structural similarity, of section pairs that are structurally consistent from Drosophila Embryo (E14-16h), between reconstruction results of PASTE, PASTE2, GPSA, STalign and ST-GEARS. b A comparison of area changes of 3 tissues and complete body of Drosophila Embryo, along cross-sectioning direction, between reconstruction result of PASTE, PASTE2, GPSA, STalign and ST-GEARS. Standard Deviation of Differences (SI-STD-DI) which measures structural consistency is marked alongside each curve to quantify the smoothness. The smoothness difference of ST-GEARS compared to PASTE, PASTE2 and STalign are highlighted by orange rectangles. c Reconstructed individual sections with recovered spatial location of each spot. In result of PASTE, the incorrect flipping on the 15th section was highlighted in orange. In result of PASTE2, gradual rotations were marked by the 1st, 5th, 9th, 13th and 16th sections’ approximate symmetry axis whereas symmetry axis of the 1st section was replicated onto the 16th for angle comparison. In result of GPSA, mistakenly distorted sections were marked by purple arrows. In result of STalign, the incorrect flipping on the 13th section was highlighted in orange. In result of ST-GEARS, the fix of dissecting area on the 15th section was marked by a blue arrow. d Dorsal view of 3D reconstructed Drosophila embryo by PASTE, PASTE2, GPSA, STalign and ST-GEARS. The inaccurate regionalization of midgut was circled and pointed with arrow in orange. The resulted extruding part of single section by PASTE2 was circled and pointed in blue. e Mapping accuracy of all section pairs by PASTE, PASTE2 and ST-GEARS. f By dorsal view, regionalization of marker gene Cpr56F and Osi7 by PASTE, PASTE2, GPSA, STalign and ST-GEARS, and their comparison with hybridization result from Berkeley Drosophila Genome Project (BDGP) database. The gathering expression regions were highlighted by dotted lines. Source data are provided as a Source Data file.

To compare the reconstruction effect, we studied both registered individual section, and reconstructed 3D volume. Among the methods compared, PASTE produced a wrong flipping on the 15 th section along A-P axis (Fig.  5c ). Stacking sections back to 3D and investigating on dorsal view, the wrong flipping caused a false regionalization of foregut circled in orange (Fig.  5d ). Along the first to last section registered by PASTE2, a gradual rotation was witnessed (Fig.  5c ), leading to over 20 degrees of angular misalignment between the first and the last section. Similar to PASTE, this misalignment also caused the wrong regionalization of foregut in 3D map (Fig.  4d ). Equally induced by the rotation, sections were found to extrude in the 3D result circled in blue, breaking the round overall morphology of the embryo. GPSA caused false distortion of 8 out of 16 sections as pointed by purple arrows (Fig.  5c ) and the stacked sections formed a dorsal view of an isolated circle and an inner region (Fig.  5d ). The phenomena may be due to its overfitting onto expressions, which is caused by the contradiction between its hypothesis of consistent readout across sections, and the large readout variation across 16 sections in this application. Similar to PASTE, STalign also produced a wrong flipping, on the 13 th section along A-P axis (Fig.  5c ). Stacking the projections back to 3D, a mistaken regionalization of foregut, caused by the wrong flipping, was circled in orange (Fig.  5d ). In contrast, ST-GEARS avoided all of these mistakes in its results (Fig.  5c ). From the perspective of individual section profiles, noticeably in the 15 th section, we observed a significant reduction in the dissecting region between two parallel lines, indicating the successful fixation of flaws in the session. By comparing time usage across all methods, ST-GEARS achieved the 2nd lowest time consumption in registration (Supplementary Fig.  11 ). In terms of memory consumption, ST-GEARS, PASTE and STalign used much less memory than PASTE2 and GPSA. The three most memory efficient methods used almost identity peak memory, with the value fluctuation of <7%.

To comprehend the rationale behind our improvement, we analyzed the anchors generated by the three methods and the impact of our elastic registration. In the investigation of anchor accuracy, we discovered that ST-GEARS achieves the highest mapping accuracy among all section pairs (Fig.  5e ), suggesting its advanced ability to generate precise anchors, which forms the basis for precise spatial profile recovery. To understand this advancement, probabilistic constraints and its resulted anchors distributions (Supplementary Fig.  16 , Supplementary Fig.  17 ) were studied. With Distributive Constraints (Supplementary Fig.  16a ), ST-GEARS generated different maximum probabilities on different annotation types (Supplementary Fig.  16b ), which indicates that annotation types with higher cross-sectional consistency were prioritized in anchor generation. This selection led to reduced computational disturbances, and hence higher accuracy of anchors. We also compared anchor accuracy with and without Distributive Constraints adopted, and noticed an increase of mapping accuracy on each pair of sections (Supplementary Fig.  18 ). In final registration result, ST-GEARS without Distributive Constraints failed to fix the experimental flaw on the 15 th section (Supplementary Fig.  19 ), in contrast to effect upon the setting adopted (Fig.  5c ). Above findings validate the contributive effect of Distributive Constraints in our method. In study of elastic registration in shape smoothness, we witnessed an increased level of smoothness of tissue epidermis, foregut, and midgut, as well as the complete section, through area changes quantified by SI-STD-DI index (Supplementary Fig.  20 ). In internal structure aspect, an increased MSSIM of structural consistent pairs were noticed (Supplementary Fig.  21 ). An experimental flaw on the 15 th section was also fixed by elastic registration (Supplementary Fig.  22 ). Above findings point that the enhancement of registration accuracy on Drosophila embryo was induced by Distributive Constraints and elastic process.

By mapping spots back to 3D space, we further investigated the effect of different method on downstream analysis, in the perspective of genes expression (Fig.  5f ). Cpr56F and Osi7 were selected as marker genes, which were found to respectively highly express in foregut, and foregut plus epidermis region 22 . Investigating Cpr56F expression by ST-GEARS from dorsal view, we noticed three highly expressing regions, at anterior end, front region, and posterior end of the embryo. The finding matches the hybridization result of stage 13-16 Drosophila embryo extracted from Berkeley Drosophila Genome Project (BDGP) database. In contrast, none of PASTE, PASTE2, GPSA and STalign presented high expression at all three locations. When analyzing the distribution of Osi7 by PASTE, PASTE2 and STalign, we noticed a sharp decrease in expression from inner region to the outer layer marked by purple arrows, contradicting the prior knowledge of high expression in the epidermis. This is probably because PASTE and PASTE2 do not consider distortion correction as part of their methods, leaving section edges un-coincided and marker genes not obviously highly expressed on the outermost region. Though involving distortion correction, STalign lost certain amount of structural information by transforming ST data to image utilizing only information of regional gene expression abundance. The registration did not adequately correct distortion without support of enough structural messages. Similarly, PASTE2 failed to capture expression in outer layers and instead revealed a high expression in one inter-connected area, which did not correspond to the separate expression regions observed in hybridization result. No spatial pattern was witnessed when analyzing distribution of Osi7 by GPSA, which forms an obvious contrast to its hybridization evidence. Comparably, none of the violations was shown in the result of ST-GEARS. The comparison of spatial distribution indicated our potential capability to better enhance the process of downstream gene-related analysis.

Application to Mouse brain reconstruction

The design of 3D experiments involves various levels of sectioning distances 22 , 36 , 37 . To further investigate the applicability of ST-GEARS on ST data with larger slice intervals, we applied the method to a complete Mouse brain hemisphere dataset, which consists of 40 coronal sections (Supplementary Fig.  23a ), with a sectioning distance of 200 μm 37 . The transcriptomics data was measured by BARseq, which includes sequencing data and its cross-modal histology images. Each observation represents captured transcriptomics surrounded by the boundary of a cell.

Through respectively applying PASTE, PASTE2, GPSA, STalign and ST-GEARS onto the dataset, we observed multiple misaligned sections produced by approaches including PASTE, PASTE2, GPSA and STalign (Supplementary Fig.  23b , Supplementary Fig.  23c , Supplementary Fig.  23d , Fig.  6a ). In PASTE, these misalignments include 2 sections with ~ 180° angular misalignment (Supplementary Fig.  23b ). By PASTE2, 4 rotational misalignments and 8 positional misalignments were noticed (Supplementary Fig.  23d ). By GPSA, 12 sections were observed to be rotationally misaligned, and 3 sections were mistakenly distorted (Supplementary Fig.  23b ), probably due to its overfitting onto expressions discussed in analysis of Drosophila embryo. The scale on horizontal and vertical axis was distorted maybe due to the similar reason analyzed in Mouse hippocampus. And by STalign, 7 rotational misalignments were generated (Supplementary Fig.  23e ). As a clear contrast, our algorithm correctly aligned all 40 sections with 200 μm intervals (Supplementary Fig.  23f ). To more accurately assess the result of our registration, we employed the direction of the cutting lines induced during tissue processing 37 , and compared the consistency of tilt angles of these lines in the 20th, 25th, 26th, 27th, 33rd, 34th and 37th slices where these lines are visible. Notably, neither visual angle differences nor cutting line curving were observed, indicating that the sections were properly aligned by ST-GEARS (Fig.  6a , Supplementary Fig.  23f ). To quantify the registration accuracy in aspect of structural continuity, we calculated MSSIM scores of 11 section pairs that are structural consistent (Fig.  6b ). Consistent with the visual observations, PASTE2 presented a much larger score range than other methods, which reflects its instability across sections in this dataset, and GPSA exhibited the lowest median MSSIM score indicating its suboptimal average performance. By comparison, PASTE yielded a higher median score and a smaller variation, while ST-GEARS resulted in the highest median score and the smallest variation among all methods. In terms of computational efficiency, ST-GEARS achieved the 2nd lowest time consumption and lowest peak memory consumption across all methods (Supplementary Fig.  11 ).

a Reconstructed individual sections with recovered spatial location of each spot from the 25th to 36th section. Positional misalignments are marked by arrows of green, and angular misalignments are marked by arrows of orange. Visible cutting lines by ST-GEARS are marked by dotted lines. b A comparison of Mean Structural Similarity (MSSIM) score of 11 section pairs that are structurally consistent, between result of PASTE, PASTE2, GPSA, STalign and our method. The 11 biological replicates were studied, which were derived from different closest section pairs with each section pair representing smallest unit of study. Non control group was used as a MSSIM close to 1 is assumed to the idealized similarity value of the structurally similar pairs, hence a higher MSSIM value indicates higher reconstruction accuracy. The red lines positions show median score; the box extends from the first quartile (Q1) to the third quartile (Q3) of scores; the lower whisker is at the lowest datum above Q1 − 0.5 * (Q3-Q1), and the upper whisker is at the highest datum below Q3 + 0.5*(Q3-Q1); scores out of whiskers range are marked by circles. c Perspective, Lateral and Anterior view of reconstructed Mouse brain hemisphere. d Anterior view of layer annotation types distribution of reconstructed Mouse brain hemisphere. Source data are provided as a Source Data file.

To understand the reasons behind our progress, we examined anchor accuracy changes with regularization factors during ST-GEARS computation (Supplementary Fig.  24 ). Out of 39 section pairs, we observed a change in mapping accuracy >0.1 (out of 1) in 12 pairs. By Self-adaptive Regularization which was designed to face varying data characteristics which also includes varying section distances, regularization factor that leads to optimal mapping accuracy was selected, leading to an increased anchors accuracy in the 12 section pairs. Notably, among these 12 pairs, pairs 29th & 30th, 31st & 32nd and 32nd & 33rd were correctly aligned by ST-GEARS but misaligned by PASTE, which doesn’t adopt any self-adaptive regularization strategy.

After validating the registration result, we investigated the recovered cell-types’ distribution in the 3D space to assess the effectiveness of the reconstruction and its impact on further analysis. We observed that the complete morphology of hemisphere was recovered by ST-GEARS, with clear distinction of different tissues on perspective, lateral and anterior views (Fig.  6c ). We further studied the distribution of separate annotation types within cortex layers and found that 3D regionalization of each annotation type was recovered by ST-GEARS (Fig.  6d ). The reconstructed result indicated the adaptability of ST-GEARS across various scales of sectioning intervals, and its applicability on both bin-level, and cell-level datasets on which histology information is incorporated.

We introduce ST-GEARS, a 3D geospatial profile recovery approach for ST experiments. Leveraging the formulation of FGW OT, ST-GEARS utilizes both gene expression and structural similarities to retrieve cross-sectional mappings of spots with same in vivo planar coordinates, referred to as ‘anchors’. To further enhance accuracy, it uses our innovated Distributive Constraints to enhance the accuracy. Then it rigidly aligns sections utilizing the anchors, before finally eliminating section distortions using Gaussian-denoised Elastic Fields and its Bi-sectional Application.

We validate counterpart of ST-GEARS including anchors retrieval and elastic registration, respectively on DLPFC and Drosophila larva dataset. In the validation of anchors retrieval, through Mapping accuracy evaluation of retrieved anchors, ST-GEARS consistently outperformed PASTE and PASTE2 across all section pairs. We show Distributive Constraints as reasons behind its distinguished performance, which effectively suppressed the generation of anchors between spot groups with low cross-sectional similarity while enhances their generation among groups with higher similarity. To investigate the effectiveness of the elastic registration process, we evaluate the effects of tissue area changes and cross-sectional similarity using the Drosophila larvae dataset. Both smoother tissue area curves and higher similarity observed between structurally consistent sections confirm the efficacy of the elastic process of ST-GEARS.

We demonstrate ST-GEARS’s advanced accuracy of reconstruction compared to current approaches including PASTE, PASTE2 and GPSA, and its positive impact on downstream analysis compared to existing approaches. Our evaluation encompasses diverse application cases, including registration of two adjacent sections of Mouse hippocampus tissue measured by Slide-seq, reconstruction of 16 sections of Drosophila embryo individual measured by Stereo-seq, and reconstruction of a complete Mouse brain measured by BARseq, including 40 sections with sectioning interval as far as 200 μm. Among the methods, registered result by ST-GEARS exhibited the highest intra-structural consistency measured by MSSIM for two hippocampus sections separated by a single layer of neurons. On 16 sections of a Drosophila embryo individual, our method’s outstanding accuracy is indicated by both MSSIM and smoothness of tissue area changes. Importantly, ST-GEARS provides more reliable embryo morphology, precise tissue regionalization, and accurate marker gene distribution under hybridization evidence compared to existing approaches. This suggests that ST-GEARS provides higher quality tissues, cells, and genes information. On Mouse brain sections with large intervals of 200 μm, ST-GEARS avoided positional and angular misalignments that occur in result of PASTE and PASTE2. The improvement was quantified by a higher MSSIM. Both hemisphere morphology and cortex layer regionalization were reflected in the result of 3D reconstruction by ST-GEARS. The successful representation of important structural and functional features in the aforementioned studies collectively underscores ST-GEARS’ reliability and capability for advancing 3D downstream research, enabling more comprehensive and insightful analysis of complex biological systems.

To further enhance and extend our method, opportunities in various aspects are anticipated to be explored. Firstly, algorithm aspects including hyperparameter sensitivity and scalability can be further explored for a more enhanced method performance. Though recommended values are provided for two of its hyperparameters, method performance is still affected by parameter values, raising the potential issue of overfitting and sensitivity which can be further studied. In scalability aspect, ST-GEARS introduces obvious computational cost increasement when dealing with large-scale datasets. Though strategy of Granularity adjusting is innovated to down-grade complexity, opportunity of improving robustness on increasing scale of data is expected to be further explored. Secondly, tasks aimed at improving data preprocessing, including but not limited to batch effect removal and diffusion correction, are expected to be integrated into our method, considering their coupling property with registration task itself: inaccuracies in input data introduce perturbations to anchors optimization, while recovered spatial information of our method may assist data quality enhancement by providing registered sections. Thirdly, the ST-GEARS’ Distributive Constraint takes rough grouping information as its input, which may potentially introduce computational burden during the reconstruction process. To address this, an automatic step is expected to be developed to reliably cluster spots while maintaining computational efficiency of the overall process. This step can be integrated into our method either as preprocessing, or as a coupling task, similarly to our expectation of data quality enhancement. Finally, we envision incorporating a wider scope of anchors applications into our existing framework. such as information integration of sections across time, across modalities and even across species. With interpretability, robustness and accuracy provided by ST-GEARS, we anticipate its applications and extension in various areas of biological and medical research. We believe that our method can help address a multitude of questions regarding growth and development, disease mechanisms, and evolutionary processes.

FGW OT description

Fused Gromov Wasserstein (FGW) Optimal Transport (OT) is the modeling of spot-wise or cell-wise similarity between two sections, with the purpose of solving optimal mappings between the spots or cells, with mappings also called ‘anchors’. By FGW OT, the optimal group of mappings enables highest gene expression similarity between mapped spots, at the same time keeping similar positions relative to their located sections.

The required input of FGW OT includes genes expression, spot or cell locations before registration, and constraint values which assigns different weight to the optimization on different spots or cells. For gene expression, we introduce \({{\bf{A}}}\in {R}^{{n}_{A},m}\) for section A, to describe normalized count of unique molecular identifiers (UMIs) of different genes of each cell or spot, thereinto n A denotes number of spots in slice A, and m denotes number of genes that are captured in both sections. Similarly, we describe gene expression on section B as \({{\bf{B}}}\in {R}^{{n}_{B},m}\) , with genes arranged in the same order as in A . For spot or cell locations, we introduce \({{{\bf{X}}}}_{{{\bf{A}}}}\in {R}^{{n}_{A},2}\) to describe spots locations of section A, with the 1st column storing horizontal coordinates and the 2nd storing vertical coordinates. Similarly, we have \({{{\bf{X}}}}_{{{\bf{B}}}}\in {R}^{{n}_{B},2}\) to describe spots locations in section B. Spots are arranged in the same order in gene expression and location matrices. Constraint values are discussed in section of Distributive Constraints.

FGW OT solves:

Thereinto, \({{{\bf{M}}}}_{{{\bf{AB}}}}\in {R}^{{n}_{A},{n}_{B}}\) describes the similarity of each pair of spots respectively on section A and B, formulated as \({{{\bf{M}}}}_{{{\bf{i}}},{{\bf{j}}}}^{({{\bf{AB}}})}={KL}({A}_{i,:},{B}_{j,:})\) . Be noted that \({{{\rm{M}}}}_{{{\rm{i}}},{{\rm{j}}}}^{({{\rm{AB}}})}\) still indicates spot-wise similarity M AB , with section code AB being moved to superscript and added parenthesis for clarity, since subscript location are taken by spot index i, j . KL denotes Kullback-Leibler (KL) divergence 43 . \({{{\bf{C}}}}_{{{\bf{A}}}}\in {R}^{{n}_{A},{n}_{A}}\) describes spot-wise distance within section A, with \({{{\bf{C}}}}_{{{\bf{i}}}{{,}}{{\bf{j}}}}^{({{\bf{A}}})}={dis}({{{\bf{X}}}}_{{{\bf{i}}}{{,}}{{:}}}^{({{\bf{A}}})}{{,}}{{{\bf{X}}}}_{{{\bf{j}}},{{:}}}^{({{\bf{A}}})})\) , and dis denoting Euclidean distance measure. Be noted that \({{{\rm{X}}}}_{{{\rm{i}}},:}^{({{\rm{A}}})}\) and \({{{\rm{X}}}}_{{{\rm{j}}},:}^{({{\rm{A}}})}\) still indicate spot locations X A , with section code A being moved to superscript and added parenthesis for clarity, since subscript location are taken by spot index i and j . \({{{\rm{C}}}}_{{{\rm{i}}},{{\rm{j}}}}^{({{\rm{A}}})}\) refers to spot-wise distance C A for the same reason. Similarly, \({{{\bf{C}}}}_{{{\bf{B}}}}\in {R}^{{n}_{B},{n}_{B}}\) describes spot-wise distance of section B. \({{\bf{L}}}\in {R}^{{n}_{A},{n}_{B},{n}_{A},{n}_{B}}\) defines the difference between all spot pair distance respectively on section A and B, with \({{{\rm{L}}}}_{{{\rm{i}}},{{\rm{j}}},{{\rm{k}}},{{\rm{l}}}}=|{{{\rm{C}}}}_{{{\rm{i}}},{{\rm{k}}}}^{({{\rm{A}}})}-{{{\rm{C}}}}_{{{\rm{j}}},{{\rm{l}}}}^{({{\rm{B}}})}|\) . ⊗ denotes Kronecker product of two matrices; 〈,〉 denotes matrix multiplication.

Adjacency matrix \({{\mathbf{\pi }}}\in {R}^{({n}_{A},{n}_{B})}\) to be optimized stores strength of anchors between spots from the two sections, with row index representing spots on section A, and column index representing spots on section B. Sum of elements of π is 1. With \({{\langle }}{{{\bf{M}}}}_{{{\bf{AB}}}}^{{{\bf{2}}}}{{,}}{{\mathbf{\pi }}}{{\rangle }}\) , the similarity of mapped spots are measured. With \({{\langle }}{{{\bf{L}}}}^{{{\bf{2}}}}({{{\bf{C}}}}_{{{\bf{A}}}}{{,}}{{{\bf{C}}}}_{{{\bf{B}}}}){{\times}}{{\mathbf{\pi }}}{{,}}{{\mathbf{\pi }}}{{\rangle }}\) , similarity between distance of spot pairs on section A, with its anchored spot pairs on section B, is measured. \(\langle {{{\bf{L}}}}^{{{\bf{2}}}}({{{\bf{C}}}}_{{{\bf{A}}}}{l{{,}}}{{{\bf{C}}}}_{{{\bf{B}}}})\otimes {{\mathbf{\pi }}}{{,}}{{\mathbf{\pi }}}\rangle\) describes similarity between spatial structures under the anchors’ connection. α ∈ [0,1] denotes regularization factor, which specifies the relative importance of structure similarity compared to expression similarity. W A and W B are constraint values that are introduced in section of Distributive Constraints.

With the formulation above, FGW OT solves optimal anchors between the spots, or cells, which enables maximum weighted combination of gene expression similarity and position similarity of mapped spots or cells.

Distributive constraints

As adopted by constraint values in FGW OT, we introduce Distributive Constraints, to assign different emphasis to spots or cells in the optimization. Distributive Constraints utilizes cell type component information to differentiate the emphasis: if an annotation or cluster express high similarity across sections, its corresponding spots or cells will be placed relatively high sum of probability, and vice versa. With higher sum of probability, more anchors and anchors with higher strength are generated, while less anchors are produced on spots with lower sum of probability. This operation leads registration to rely more on expression-consistent regions of sections, hence largely enhancing both accuracy of anchors and precision of following rigid and elastic registration.

The required inputs of Distributive Constraints include \({{{\bf{G}}}}_{{{\bf{A}}}}\in {R}^{{n}_{A}}\) and \({{{\bf{G}}}}_{{{\bf{B}}}}\in {R}^{{n}_{B}}\) , which store the grouping information such as annotation type or cluster of each spot in section A and B. We then summarize the repeated annotations or clusters from G A and G B , and put the unique values in \({{\bf{g}}}\in {R}^{{n}_{{group}}}\) . n group is the number of unique annotation type or clusters. Then implemented in ST-GEARS, for each annotation type or cluster g i , we calculate the average gene expression across spots:

Be noted that \({{{\bf{G}}}}_{{{{\bf{i}}}}^{{\prime} }}^{({{\bf{A}}})}\) and \({{{\bf{G}}}}_{{{{\bf{i}}}}^{{\prime} }}^{({{\bf{B}}})}\) still indicate grouping information G A and G B , with section code A and B being moved to superscript and added parenthesis for clarity, since subscript location are taken by spot index i ′ and j ′. And \({{\bf{1}}}_{{{{\bf{n}}}}_{{{\bf{A}}}}}\) and \({{\bf{1}}}_{{{{\bf{n}}}}_{{{\bf{B}}}}}\) are both row vectors of ones.

With average gene expression of each annotation type or cluster, with the form of distribution, we measure its difference across sections by KL divergence. Then the calculated distance is mapped by logistic kernel, to further emphasize differences between relatively consistent annotations or clusters.

\({di}{s}_{{map}}={f}_{{logistic}}({dis})\) , where \({f}_{{logistic}}\left(x\right)=\frac{1}{1+{e}^{-x}}-0.5\) . Putting scaler value dis of each annotation or cluster together, we have a vector \({{\bf{DI}}}{{{\bf{S}}}}_{{{\bf{map}}}}\in {R}^{{n}_{{celltype}}}\) . Finally, we transform the distance to similarity, map the similarity result back to each spot:

We further apply normalization on the result:

W A and W B are constraints values applied in (1). Since the values are computed based on similarity measure using cell composition information, weight of FGW OT is automatically redistributed, with higher emphasis on more consistent regions across sections, and less emphasis on less consistent area. Enhanced anchor accuracy hence registration accuracy is then achieved.

Self-adaptive regularization

In FGW OT formulation, a regularization factor is included to specify the relative importance of structural similarity compared to expression similarity during optimization. ST-GEARS includes a self-adaptive regularization method that determines the factor value, that induces highest overall accuracy of anchors despite of varying situations. Situations include but are not limited to section distances, spot sizes, extent of distortions, and data quality such as level of diffusion.

By practice, our method respectively adopts factors on multiple scales including 0.8, 0.4, 0.2, 0.1, 0.05, 0.025, 0.013, and 0.006. The candidate values vary exponentially, for ST-GEARS to find the optimal term regardless of scale differences between expression and structural term in (1). The accuracy of each set of optimized anchors by every regularization factor was evaluated, by measuring weighted percentage \({\sum}_{{{{\bf{G}}}}_{{{\bf{i}}}}^{{{(}}{{\bf{A}}}{{)}}}{{=}}{{{\bf{G}}}}_{{{\bf{j}}}}^{{{(}}{{\bf{B}}}{{)}}}}{{{\boldsymbol{\pi }}}}_{{{\bf{i}}}{{,}}{{\bf{j}}}}\) of anchors that join spots with same annotation types or clusters. Be noted that \({{{\rm{G}}}}_{{{\rm{i}}}}^{({{\rm{A}}})}\) and \({{{\rm{G}}}}_{{{\rm{j}}}}^{({{\rm{B}}})}\) still indicate grouping information G A and G B , respectively, with section code A and B being moved to superscript and added parenthesis for clarity, since subscript location are taken by spot index i and j . The regularization factor value that achieves highest accuracy is then adopted by our method.

Elastic field inference

Finding spots with highest probability.

After rigid registration, elastic fields are inferred based on the anchors with the highest probability for each spot or cell. For elastic field to be applied on each section, it is calculated using its anchors with closest sections, as well as spatial coordinates of sections after rigid registration. Along cross-sectioning order, each section in the middle has two closest sections, respectively on its anterior and posterior sides. Exceptionally, if a section is on anterior or posterior end, it has only one closest section.

Specifically for a section in the middle with N spots, we calculate \({{{\bf{I}}}}_{{{\bf{pre}}}}\epsilon {Z}^{N}\) and \({{{\bf{I}}}}_{{{\bf{next}}}}\epsilon {Z}^{N}\) which stores the mapped spots on anterior and posterior neighbor section for each of its spots. The calculation takes as input adjacency matrix π pre , which stores anchors with the anterior neighbor section output by FGW OT, and π next storing anchors with posterior section.

Be noted that \({{{\rm{\pi }}}}_{:,{{\rm{n}}}}^{\left({{\rm{pre}}}\right)}\) and \(\,{{{\rm{\pi }}}}_{{{\rm{n}}},:}^{({{\rm{next}}})}\) still indicate adjacency matrix π pre and π next , with direction code pre and next being moved to superscript and added parenthesis for clarity, since subscript location are taken by spot index n .

Notably, not every spot in a selected section has its own anchored spot, due to multiple strategies including distributive constraint and anchors filtration, hence their corresponding element in I pre and I next are null. For section located on posterior end, only I next is applicable; and for section located on anterior end, only \({{{\bf{I}}}}_{{{\rm{pre}}}}^{{{\rm{n}}}}\) is applicable.

Elastic field establishment

After specifying spots with highest probability, ST-GEARS calculates location displacements between the spots, then establishes elastic fields for each section. An elastic field is a 2D displacement distribution, describing how displacement values are distributed across different locations. And it is established to enable ST-GEARS to benefit from further denoising functions to reduce elastic operation outliers and improve elastic effect consistency across regions.

For each section located in the middle, 4 elastic fields are generated. Two of those represent the section’s horizontal and vertical displacement distribution compared to anterior neighbor section, denoted as 2D matrix F (x_pre) and F (y_pre) , while the other two represent its horizontal and vertical displacement distribution compared to posterior neighbor, denoted as F (x_next) and F (y_next) . To initialize F (x_pre) , F (y_pre) , F (x_next) and F (y_next) for the section, the shape of the matrix is first decided. Its height denoted by Height and width denoted by Width are calculated by gridding the spot locations using a fixed step. Height and Width are shared across the 4 matrices:

For its input, \({{\bf{X}}}\in {R}^{N,2}\) denotes spots location of current section after rigid registration. For a single section, we prepare \({{{\bf{X}}}}^{{{(}}{{\bf{pre}}}{{)}}}\epsilon {R}^{{N\_pre},2}\) and \({{{\bf{X}}}}^{{{(}}{{\bf{next}}}{{)}}}\epsilon {R}^{{N\_next},2}\) as spots location of its anterior and posterior section after rigid alignment, respectively. psize represents average distance between closest spot or cell centers, and it is to be input by users. The matrix has no filled values to this step.

To fill in the fields, we first transform spot locations into the coordinate system of field. With \({{\bf{X}}}\_{{\bf{shifted}}}\,\epsilon {R}^{N,2}\) and \({{\bf{X}}}\_{{\bf{pixel}}}\,\epsilon {R}^{N,2}\) :

We then calculate location displacements between each of its spots and their anchored spots with highest probability, on both anterior and posterior neighbors. With \({{\bf{X}}}\_{{\bf{corres}}}\,\epsilon {R}^{N,2}\) and \({{\bf{X}}}\_{{\bf{delta}}}\epsilon {R}^{N,2}\) :

With the spot locations in field coordinates and the displacement values above, we fill in corresponding elements of the elastic field:

By the end of Eqs. ( 2 ), 4 elastic fields for each section in the middle is established. However, some elements in the matrix are still empty, because of absence of spots or cells located in the grid of location. To address this problem, 2d nearest interpolation method 44 was adopted, which fills in every empty element, with the displacement value of its neighboring elements:

thereinto \({{\bf{mes}}}{{{\bf{h}}}}_{{{\bf{trans}}}}\epsilon {N}^{{n}_{{grids}}\times 2}\) denotes grid coordinates of the designed field, with \({n}_{{grids}}={Height}\times {Width}\) . And f interp_grid denotes the nearest interpolation method.

For section located on posterior end, only F (x_next) and F (y_next) are applicable; and for section located on anterior end, only F (x_pre) and F (y_pre) are applicable.

2D Gaussian denoising

As caused by exerted force, the displacement or elastic field is expected to have static or smoothly changing values across different locations 45 , 46 , 47 . ST-GEARS makes use of this property, to smoothen the field and to reduce errors in the field caused by any upper stream process, such as raw data noises and inaccuracy in anchor computation. Gaussian filtering 48 , 49 is adopted to implement the denoising, similarly to image denoising processes 50 , 51 . Denoised elastic fields are then generated.

It calculates weighted average across the neighboring region of each element to replace its value:

where f gaussian_filter denotes the method of Gaussian filtering.

Bi-sectional fields application

Bi-sectional fields application plan.

With elastic fields generated and denoised, ST-GEARS uses the fields as a guidance to correct distortion for each section. Through querying the elastic fields with spatial location of each spot, the displacement to be implemented is returned. For a section in the middle, its elastic fields calculated with both anterior and posterior neighbor sections are queried, and guidance provided by both anterior and posterior sections are applied on the rigid aligned result, called ‘Bi-sectional Fields Application’. After the application, the distortion of the section is corrected, and the elastic registration result is generated.

Specifically, the denoised elastic fields are first queried, returning the displacement to be implemented:

Next, average displacement returned by both anterior and posterior sections are applied on the rigid registration result, leading to final elastic registration result \({{\bf{X}}}\_{{\bf{final}}}\in {R}^{N,2}\!\!:\)

For section located on posterior end,

For section located on anterior end,

The validity of this plan is proved in the section: Proof of validity of Bi-sectional Fields Application.

Proof of validity of Bi-sectional fields application

Bi-sectional Fields Application accurately recovers the spatial profile before distortion, by averaging and applying displacement value guided by both anterior and posterior neighbor section. The effect is approved mathematically as following:

Take section A, B, and C as an example of a sequence of sections, with X A , X B and X C denoting their spots’ spatial information after rigid alignment, and X A_insitu , X B_insitu and X C_insitu denoting their in vivo spatial information. The distortion occurred to the slices during experiments are denoted as X A_dis , X B_dis and X C_dis .

According to Bi-sectional Fields Application, the corrected spatial information is:

Based on the in vivo morphological consistency across sections, spatial information of section B can be approximated by an average of information of A and C, written as

Given that X A_dis and X C_dis can be seen as independent and identically distributed sets of variables,

where μ ABC is the universal mean, and Σ ABC is the variance of the 2d displacement information.

Inserting the terms (4) and (5) back to Eq. ( 3 ) gives

indicating the proximity of corrected spatial information to in vivo spatial information.

Evaluation metrix

We evaluated the accuracy of anchors by index of Mapping Accuracy, and measured the reconstruction effect by MSSIM and SI-STD-DI, in both elastic effect study and overall methodology comparison.

Mapping accuracy

Designed and adopted by PASTE 27 , Mapping Accuracy calculates the weighted percentage of anchors joining spots with same annotation.

MSSIM index

MSSIM measures the accuracy of registration, based on the assumption that in some sectioning positions, tissue morphology remains almost consistent across slices. The method quantifies the accuracy, by measuring the similarity of annotation type distribution of such section pairs.

To implement the quantification, first, structurally consistent section pairs are selected among all sections arranged in sequence.

Next, on each section from the pair, transformation from individual spots to a complete image is implemented, by gridding the rectangular area that surrounds the tissue, and assigning each grid of a value that represents the annotation type which occurs most frequently in the grid. The resulted image describes the annotation type distribution of the section.

Finally, similarity between each pair of images is measured, by index of MSSIM 52 . The method generates a window with fixed size, slides the window simultaneously on both images, and compares the two framed parts by windows on their intensity, contrast, and structures. Among those, the intensity difference is measured by difference of average pixel values, the contrast difference is measured by comparing variance of the two sets of framed pixel values, and the structure difference is measured by comparing their covariances. A Structural Similarity of Images (SSIM) index is calculated for each position of the window using \({SSIM}(X,Y)=\frac{(2{\mu }_{x}{\mu }_{y})(2{\sigma }_{{xy}}+{c}_{2})}{({\mu }_{x}^{2}+{\mu }_{y}^{2}+{c}_{1})({\sigma }_{x}^{2}+{\sigma }_{y}^{2}+{c}_{2})}\) , where μ x and μ y denote average pixel values of the frames, σ x and σ y denote variances of the frames, and σ xy denotes covariances of the two frames. c 1 and c 2 are constants to avoid 0 value of the divisor. Averaging the SSIM value across all windows gives the final MSSIM result of the two sections.

SI-STD-DI measures smoothness of area changing across sections along a fixed axis, by calculating the standard deviation of area changes on each pair of adjacent sections and scale the result by dividing it by average area.

Software and code

Data analysis.

All software used to analyze data in this study are open-sourced Python packages, including anndata = 0.9.2, numpy = 1.22.4, pandas = 1.4.3, scipy = 1.10.1, matplotlib = 3.5.2, k3d = 2.15.3.

Statistics and reproducibility

No statistical method was used to predetermine sample size. No data were excluded from the analyses. The experiments were not randomized. The Investigators were not blinded to allocation during experiments and outcome assessment.

Reporting summary

Further information on research design is available in the  Nature Portfolio Reporting Summary linked to this article.

Data availability

All data used in this research were collected from published sources. DLPFC data was obtained from the research: Transcriptome-scale Spatial Gene Expression in the Human Dorsolateral Prefrontal Cortex, with data downloading link of http://research.libd.org/spatialLIBD/index.html ; Drosophila embryo and Drosophila larva data were collected from High-resolution 3d Spatiotemporal Transcriptomic Maps of Developing Drosophila Embryos and Larvae, with the dataset link of https://db.cngb.org/stomics/datasets/STDS0000060 . Mouse brain data was collected from research: Modular cell type organization of cortical areas revealed by in vivo sequencing. The download link is: https://data.mendeley.com/datasets/8bhhk7c5n9/1 . All datasets were generated on Spatial Transcriptomics platform, with DLPFC data generated by Visium technology of 10x Genomics, Mouse brain data generated by BARseq of Cold Spring Harbor Laboratory, while Drosophila embryo and larva generated by Stereo-seq technology of BGI.  Source data are provided with this paper.

Code availability

The methods of ST-GEARS is packaged, and distributed as an open-source, publicly available repository at https://github.com/STOmics/ST-GEARS 53 .

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Acknowledgements

This work is part of the “SpatioTemporal Omics Consortium” (STOC) paper package. A list of STOC members is available at: http://sto-consortium.org . We acknowledge the Stomics Cloud platform ( https://cloud.stomics.tech/ ) for providing convenient ways for analyzing spatial omics datasets. We acknowledge the CNGB Nucleotide Sequence Archive (CNSA) of China National GeneBank DataBase (CNGBdb) for maintaining the Drosophila database. This work is supported by National Natural Science Foundation of China (32300526 to S. F., 32100514 to M. X.). We thank Weizhen Xue for the inspirational discussion towards design of Distributive Constraints. We thank Yating Ren for her advice towards a more efficient code implementation. We thank Dr. Xiaojie Qiu and Dr. Yinqi Bai for the discussion on the registration topic and their advice on our work.

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Tianyi Xia, Luni Hu, Lei Cao, Yunjia Zhang, Lei Zhang, Taotao Pan, Bohan Zhang, Bowen Ma, Chuan Chen, Chao Liu & Shuangsang Fang

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Tianyi Xia was responsible of method design, analysis design and implementation, as well as drafting of this manuscript. Dr. Luni Hu participated in structure design of the applications. Lulu Zuo was in part of 3D visualizations design, and she helps maintain our online repository. Tianyi Xia, Lei Cao, Lulu Zuo and Dr. Luni Hu conducted experiments and analysis for reply to peer review. Dr. Yunjia Zhang provided insights in anchors results interpretation of DLPFC dataset, and in accuracy analysis of mouse brain dataset. Dr. Mengyang Xu revised this article. Lei Zhang and Bowen Ma offered numerous suggestions to enhance computational efficiency, in both memory and time. Taotao Pan and Chuan Chen provided suggestions in data preprocessing. Qin Lu, Bohan Zhang, Junfu Guo, Chang Shi and Mei Li provided suggestions for this study. Dr. Shuangsang Fang supervised this study in structure and analysis design, and she revised this article. Chao Liu, Yuxiang Li and Yong Zhang supervised this study.

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Xia, T., Hu, L., Zuo, L. et al. ST-GEARS: Advancing 3D downstream research through accurate spatial information recovery. Nat Commun 15 , 7806 (2024). https://doi.org/10.1038/s41467-024-51935-0

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Drug–Drug Interactions Involving High-Alert Medications that Lead to Interaction-Associated Symptoms in Pediatric Intensive Care Patients: A Retrospective Study

Lisa marie kiesel.

1 Clinical Pharmacy, Institute of Pharmacy, Medical Faculty, Leipzig University, and Drug Safety Center, Leipzig University and Leipzig University Hospital, Leipzig, Germany

Astrid Bertsche

2 Division of Neuropediatrics, University Hospital for Children and Adolescents, Greifswald, Germany

3 Center for Pediatric Research, University Hospital for Children and Adolescents, Leipzig, Germany

Wieland Kiess

Manuela siekmeyer, thilo bertsche, martina patrizia neininger, associated data.

Children treated in a pediatric intensive care unit (PICU) often receive several drugs together, among them drugs defined as high-alert medications (HAMs). Those drugs carry a high risk of causing patient harm, for example, due to a higher potential for interactions. HAMs should therefore be administered with caution, especially in a PICU.

The objective of the current study was to identify drug–drug interactions involving HAMs that increase the risk of interaction-associated symptoms in pediatric intensive care.

In a retrospective study, we analyzed the electronic documentation of patients hospitalized for at least 48 h in a general PICU who received at least two different drugs within a 24-h interval. We assessed potential drug–drug interactions involving HAM on the basis of the two drug information databases UpToDate and drugs.com. Furthermore, we analyzed whether symptoms were observed after the administration of drug pairs that could lead to interaction-associated symptoms. For drug pairs involving HAM administered on at least 2% of patient days, and symptoms observed at least ten times after a respective drug pair, we calculated odds ratios, 95% confidence intervals, and p -values by using a univariate binary logistic regression.

Among 315 analyzed patients, 81.3% (256/315) received drugs defined as high-alert medication for pediatric patients. Those high-alert medications were involved in 20,150 potential drug–drug interactions. In 14.0% (2830/20,150) of these, one or more symptoms were observed that could be a possible consequence of the interaction, resulting in 3203 observed symptoms affecting 56.3% (144/256) of patients receiving high-alert medication. The odds ratios for symptoms observed after a drug–drug interaction were increased for eight specific symptoms (each p ≤ 0.05), especially hemodynamic alterations and disturbances of electrolyte and fluid balance. The odds ratio was highest for decreased blood pressure observed after the administration of the drug pair fentanyl and furosemide (OR 5.06; 95% confidence interval 3.5–7.4; p < 0.001). Increased odds ratios for specific symptoms observed after drug–drug interactions resulted from eight combinations composed of eight different drugs: digoxin, fentanyl, midazolam, phenobarbital, potassium salts and vancomycin (high-alert medications), and the diuretics furosemide and hydrochlorothiazide (non-high-alert medications). The resulting drug pairs were: potassium salts–furosemide, fentanyl–furosemide, vancomycin–furosemide, digoxin–furosemide, digoxin–hydrochlorothiazide, fentanyl–phenobarbital, potassium salts–hydrochlorothiazide, and midazolam–hydrochlorothiazide.

Conclusions

In a cohort of PICU patients, this study identified eight specific drug pairs involving high-alert medications that may increase the risk of interaction-associated symptoms, mainly hemodynamic alterations and electrolyte/fluid balance disturbances. If the administration of those drug pairs is unavoidable, patients should be closely monitored.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40272-024-00641-x.

Introduction

Children admitted to a pediatric intensive care unit (PICU) are often in a critical state of health and require complex drug treatment. Although administration of multiple drugs together leads to an increased risk of drug-related problems [ 1 , 2 ], previous studies reported most patients in PICUs received a median number of ten different drugs per patient day [ 3 , 4 ]. Especially in the PICU, so-called high-alert medications (HAMs) must be administered frequently. Due to various factors, such as a narrow therapeutic range or a high potential for drug–drug interactions (DDIs) [ 5 , 6 ], these drugs bear a higher risk of causing patient harm compared with other drugs, according to the Institute for Safe Medication Practices (ISMP) [ 7 ]. Therefore, the administration of HAM should be given careful consideration. The ISMP developed its first list of HAMs for the acute care setting in 1995 [ 7 ]. Until now, few studies have identified specialized lists of HAM for children [ 6 , 8 – 10 ]. Schilling et al. combined results from three previous studies to develop a list of 20 HAMs for pediatric patients in the German setting. They described DDI as a drug-related problem for half of those 20 [ 6 ].

There is scant literature about DDIs involving HAMs for pediatric patients or their implications for children admitted to a PICU. Therefore, we aimed to identify DDIs involving HAMs that may increase the risk of interaction-associated symptoms. We specifically targeted drug pairs that should be avoided in daily clinical practice or closely monitored if their administration is unavoidable. We did not distinguish between different severity grades for DDI and symptoms, as we aimed to assess the most common DDIs regardless of their classification according to the databases, and we endeavored not to overlook any relevant symptoms. Therefore, we also included drug–drug interactions with a low classification according to the databases, as these can also severely affect patients in a critical health state.

Material and Methods

Study design.

This retrospective study analyzed data from April 2018 to March 2019 obtained in a general PICU of a university hospital in Germany. Patients of all pediatric age groups were treated in the study unit, except neonates, who were treated in a separate neonatal intensive care unit. We assessed the electronic documentation for each patient in the hospital’s patient data management system to identify potential DDIs (pDDIs) involving at least one drug defined as a HAM. Furthermore, we analyzed symptoms observed after these pDDIs to detect interaction-associated symptoms.

We included patients hospitalized for at least 48 h in the study unit who received at least two different drugs within a 24-h interval during their stay. Patients on chemotherapy were excluded because they were mainly treated at the pediatric oncology unit of the university hospital and only transferred to the PICU for a short time if their health condition deteriorated severely.

The study titled “Adverse drug reactions in an interdisciplinary PICU” was conducted according to the guidelines of the Declaration of Helsinki and approved by the Ethics Committee at the Medical Faculty, Leipzig University, Germany (study number: 127/19-ek) on 2 May 2019. The ethics committee waived informed consent because this was a retrospective study, and patients’ treatment was not influenced.

Identification of HAM in the PICU and pDDIs Involving at Least One HAM in Clinical Care

On the basis of the electronic patient documentation, we first examined the administration of the 20 drugs that Schilling et al. [ 6 ] defined as HAM for hospitalized pediatric patients. We included 15 of those HAMs in our analysis because 5 of the 20 defined HAMs were not administered in our PICU during the study period: cyclosporine, phenytoin, amiodarone, vecuronium, and rocuronium. Second, we evaluated pDDIs that involved at least one HAM for each patient day. For this purpose, potentially interacting drug pairs were identified on the basis of two drug information databases: UpToDate (provided by Wolters Kluwer, Riverwoods, Illinois, USA) and drugs.com (provided by Drugsite Trust, Auckland, New Zealand). Each drug pair for which an interaction alert was reported in at least one of the databases was defined as a pDDI. To identify potentially interacting drug pairs, we considered a maximum time interval of 24 h between administering a HAM and another potentially interacting drug, regardless of whether the second drug was defined as a HAM. We considered a 24-h interval to be appropriate because estimating the correct half-lives of interacting drugs in individual patients in our cohort was hardly feasible due to the general developmental variability of pharmacokinetics and pharmacodynamics in children and the possible influence of the individual patient’s condition. Hence, if a potentially interacting drug was administered 24 h before or after a HAM, the event was categorized as a pDDI. If a pDDI occurred more than once within the defined time interval, it was counted only once. For continuous infusions, it was assessed for each drug administered concurrently whether a pDDI occurred due to the additional drug.

Identification of Interaction-Associated Symptoms

For each pDDI, we investigated whether symptoms that could be associated with it were observed after the administration. For this investigation, we examined the nurses’ and physicians’ daily documentation of the patient’s condition for symptoms that occurred within a 24-hour interval after administration of the second drug of the relevant drug pair. The documentation included automatically recorded vital parameters, laboratory parameters, and additional documentation, such as non-measurable symptoms as nausea or vomiting (Online Resource 1). For vital and laboratory parameters, age-dependent standard ranges for infants, children, and adolescents were determined by the treating physicians. For some patients, the attending physician adjusted the standard ranges to the patient’s health condition. In our analyses, we considered deviations from the determined patient–individual ranges. We focused on symptoms that were identified as possible consequences of a pDDI according to our database search in UpToDate and drugs.com. If at least one of these symptoms was associated with the relevant drug pair at a statistically significant odds ratio (OR), this was defined as a DDI. Since we took the underlying data on the symptoms from the documentation of physicians and nursing staff, it can be assumed that those symptoms were clinically relevant, as they would otherwise not have been documented.

To estimate the risk associated with the interaction of a particular drug pair for an observed symptom, we calculated the OR and 95% confidence interval. To ensure that the calculation was based on a sufficient occurrence of a particular drug pair and corresponding symptom, we set two criteria. First, we only considered potentially interacting drug pairs administered on at least 2% of patient days. Second, we focused only on corresponding symptoms observed at least ten times after a given drug pair. Combining potentially interacting drug pairs and symptoms that met these criteria, we created a contingency table that presents the frequency of the following combinations on each patient day: both the potentially interacting drug pair and corresponding symptom were observed; only the potentially interacting drug pair was observed; only the symptom was observed; and neither the potentially interacting drug pair nor the symptom was observed. On the basis of the contingency table, a univariate logistic regression was performed to obtain OR, 95% confidence interval, and p -value. The calculation was conducted using IBM SPSS Statistics Version 29 (IBM Corporation, Armonk, New York, USA). A p value ≤ 0.05 was considered to indicate significance.

Characteristics of Patients and Administered Drugs

We examined 1263 patients admitted to the PICU during the study period for the inclusion criteria (Fig. ​ (Fig.1). 1 ). Of those, 315 (24.9%) patients fulfilled the inclusion criteria. Baseline patient characteristics are presented in Table ​ Table1. 1 . In total, 255 different drugs were administered to the patients. Of these drugs, 5.9% (15/255) were identified as HAM for hospitalized pediatric patients, according to the study by Schilling et al. [ 6 ] (Table ​ (Table2). 2 ). The most commonly administered sedative during the study period was midazolam [affected 173/315 (54.0%) patients on 1011/3788 (26.7%) patient days; Online Resource 2]. Potassium salts were the most frequently administered HAM, used on 39.0% of patient days (1477/3788), in 47.3% (149/315) of patients (Table ​ (Table3 3 ).

Flow chart of patient inclusion

Baseline patient characteristics

PICU pediatric intensive care unit

Characteristics of drug therapy

HAM high-alert medication

Frequency of high-alert medications administered in the pediatric intensive care unit during the study period. In our analysis, we included 15 of 20 drugs defined as high-alert medications for hospitalized pediatric patients according to Schilling et al. [ 6 ]

The remaining five high-alert medications were not administered during the study period: cyclosporine, phenytoin, amiodarone, vecuronium, and rocuronium

pDDIs Involving at Least One HAM

Analyzing each patient’s electronic documentation, we identified 20,150 pDDIs involving at least one HAM on the basis of our database search in UpToDate and drugs.com. We calculated a rate of 78.7 pDDIs per patient that involved at least one HAM (20,150 pDDI involving at least one HAM/256 patients receiving HAM). The 20,150 pDDIs resulted from 469 different drug pairs. Of these potentially interacting drug pairs, 14.3% (67/469) were administered on at least 2% of patient days. The frequency of the potentially interacting drug pairs and their classifications according to the databases is presented in Online Resource 3.

Interaction-Associated Symptoms Identified in the PICU

We observed at least one symptom after 14.0% (2830/20,150) of pDDIs, resulting in a total of 3203 observed symptoms affecting 56.3% (144/256) of patients receiving HAM (Table  4 ). While we observed one symptom after the administration of 87.7% (2482/2830) of those pDDIs, more than one symptom was observed after 12.3% (348/2830) of pDDIs.

Frequency of symptoms observed after potential drug–drug interactions involving high-alert medications

HAM high-alert medication, pDDI potential drug–drug interaction, PTH parathyroid hormone

The most pDDIs after which we observed at least one symptom involved potassium salts (2.4%; 493/20,150), followed closely by digoxin (2.4%; 480/20,150) and fentanyl (2.4%; 476/20,150; Fig. ​ Fig.2 2 ).

For each high-alert medication, the number of potential drug–drug interactions (total interactions: N = 20,150) is plotted against how often at least one symptom was observed after a potential drug–drug interaction involving the respective high-alert medication (total interactions followed by symptoms: N = 2830)

For 33.1% (1061/3203) of observed symptoms, the preconditions for the calculation of the OR were fulfilled (Table ​ (Table5). 5 ). We found an increased OR for hyponatremia, hypokalemia, decreased blood pressure, increased heart rate, urinary retention, edema, sweating, and restlessness (each p ≤ 0.05; Table ​ Table5). 5 ). Those eight specific symptoms accounted for 28.0% (897/3203) of all observed symptoms potentially related to DDI. These DDIs involved eight different drugs in eight different combinations. Of the eight drugs, 75% (6/8) were defined as HAM for pediatric patients: digoxin, fentanyl, midazolam, phenobarbital, potassium salts, and vancomycin. The remaining 25% (2/8) were diuretics not defined as HAM: furosemide and hydrochlorothiazide. The highest OR was found for decreased blood pressure observed after administration of the drug pair fentanyl and furosemide (OR 5.06; 95% CI 3.5–7.4; p < 0.001), followed by hypokalemia observed after administration of the drug pairs digoxin and furosemide (OR 4.16; 95% CI 3.1–5.6; p < 0.001) and digoxin and hydrochlorothiazide (OR 3.86; 95% CI 2.9–5.1; p < 0.001).

Drug–drug interactions involving high-alert medications and subsequent symptoms observed within 24 h after the administration of the respective drug–drug interaction

For each drug combination and observed symptom, the frequencies of patient days on which the respective potential drug–drug interaction was or was not administered and whether the symptom was observed is shown. From those numbers, the odds ratios, 95% confidence intervals, and p -values were calculated using a univariate logistic regression

HCT hydrochlorothiazide, n/a not applicable (not listed in the respective database), pDDI potential drug–drug interaction

*Significant

a Categorized as high-alert medication for hospitalized pediatric patients according to Schilling et al. [ 6 ]

b Classification used in UpToDate: “D—Consider therapy modification; C—Monitor therapy; B—No action needed. Agents may interact with each other”

c Classification used in Drugs.com: “Major—Avoid combinations; Moderate—Usually avoid combination. Use it only under special circumstances; Minor—Take steps to circumvent the interaction risk and/or establish a monitoring plan”

HAMs are Common Drugs Administered in the PICU

According to the ISMP, HAMs carry a higher risk of patient harm compared with ordinary drugs [ 7 ]. Even when used as prescribed, they significantly increase the risk of drug-related problems [ 11 ]. In our study, 81% of critically ill children received at least one drug defined as HAM for pediatric patients by Schilling et al. [ 6 ]. Potassium salts, midazolam, and vancomycin were the HAMs most frequently administered. This is in line with a previous study in a pediatric emergency setting reporting that 91% of patients were prescribed at least one HAM, with potassium salts being the most frequently administered [ 12 ].

More than 20,000 pDDIs with HAM During a 1-Year Study

It is widely known that pDDIs are highly prevalent in PICUs. They are associated with various factors, such as a high number of administered drugs, a complex chronic condition, or an increased length of hospitalization [ 4 , 13 , 14 ]. Although previous studies determined pDDI as a cause of drug-related problems with HAM for pediatric patients, there is only limited knowledge about the frequency of pDDIs in pediatric intensive care [ 6 , 8 , 10 ]. In our study, we found more than 20,000 pDDIs involving HAM in 256 pediatric patients over the 1-year study period. A previous Brazilian study of adult intensive care patients reported 846 HAM-related pDDIs in 60 patients [ 15 ]. Compared with our research, the Brazilian study reported a considerably lower rate of HAM-related pDDIs per patient (79 versus 14). Part of this difference may be explained by the fact that pediatric patients requiring intensive care are more susceptible to drug–drug interactions [ 16 ]. However, it may also be related to the fact that the Brazilian study was performed on the basis of the database Micromedex 2.0 only [ 15 ]. Several studies recommended using at least two databases to determine pDDIs in daily routine [ 17 – 19 ] . Thus, we used the two databases, UpToDate and drugs.com, to avoid underestimating any potential risks. However, since the concordance between different databases is limited, comparing various studies can be challenging [ 20 , 21 ].

Physicians Should be Aware of Interaction-Associated Symptoms

For 2830 pDDIs, we observed 3203 symptoms occurring after the administration of the potentially interacting drug pairs. More than one in four detected symptoms were eventually associated with a DDI. Those interaction-associated symptoms comprised eight specific symptoms, mainly hemodynamic alterations or electrolyte and fluid balance disturbances. These symptoms were frequently reported in previous pediatric intensive care studies [ 3 , 22 – 24 ]. The study presented here shows that DDI involving HAM should be considered a likely trigger for symptoms in addition to other factors, such as the underlying disease or non-drug treatments, such as surgeries. It can also be assumed that various factors contribute to the occurrence of a symptom. When identifying DDIs and following interaction-associated symptoms, we did not distinguish between different severity grades of DDI or symptoms, as the main aim of our study was to identify drug pairs that are frequently associated with symptoms that are considered clinically relevant by the responsible physicians and nurses. Physicians usually receive a considerable number of alerts when using a database-related interaction checker. This may quickly lead to over-alerting. Therefore, we aimed to provide physicians with a concise overview of clinically relevant DDIs that occur frequently in a PICU. Our findings could be implemented in commonly used database-related interaction checkers to draw physicians’ attention to drug pairs involving HAM that are potentially associated with an increased risk of adverse events.

We identified eight specific drug pairs composed of eight different drugs that may lead to an increased risk of interaction-associated symptoms. By calculating the OR for a DDI and a respective symptom, we took into account how often a symptom was observed on patient days when the interacting drug pair was administered compared with days when the respective drug pair was not administered. In particular, this should minimize the risk that certain combinations of DDI and symptoms are over- or underestimated. For the interaction of fentanyl and furosemide, we found the highest OR for the symptom of decreased blood pressure. Both drugs have been shown to belong to the top ten of the most frequently administered drugs and to be among the drugs most commonly involved in pDDIs in the pediatric intensive care setting [ 4 ]. In our study, DDI was associated with a potential fivefold increased risk of decreased blood pressure. The second highest OR, indicating a potential fourfold increased risk, was found for the interaction of digoxin with hydrochlorothiazide and the observed symptom of hypokalemia. Consequently, when the administration of drug pairs associated with a potentially increased risk of interaction-associated symptoms is unavoidable, patients should be closely monitored for potential symptoms.

Until now, few studies have dealt with interaction-associated symptoms in the pediatric intensive care setting [ 14 , 25 , 26 ]. One of those studies only focused on cytochrome P450-mediated drug–drug interactions [ 25 ]. Two other studies concentrated on symptoms on the basis of clinical monitoring and laboratory results, as we did in our research. Both studies also identified hemodynamic alterations and electrolyte and fluid balance disturbances as symptoms following DDIs. However, neither of those studies noted specific interactions that increased the risk of the detected symptoms [ 14 , 26 ]. Our study went one step further by revealing eight interacting drug pairs that may increase the risk of the identified interaction-associated symptoms in clinical practice. We found symptoms that are widely known to follow the respective DDI, such as the association of hyponatremia with the DDI of potassium salts and furosemide, or the increased risk for hypokalemia associated with the DDI of digoxin and furosemide. However, we also observed symptoms after a DDI that we did not expect. For example, we unexpectedly found that the DDI of fentanyl and furosemide was associated with a potential risk increase for urinary retention, or that the DDI of vancomycin and furosemide was associated with edema. Especially for symptoms that unexpectedly are observed after a specific DDI, other factors, such as the state of illness or a surgery that could also lead to the symptom, should be critically evaluated.

Limitations

Some limitations have to be considered when interpreting our study results. First of all, the relevance of some drugs administered in our study can vary in different PICUs around the world. However, the 15 drugs defined as HAM that were in the focus of our study are used in many PICUs worldwide [ 4 , 27 – 31 ].

As recommended by previous studies [ 17 – 19 ], we used two databases to prevent failure to detect interactions that could lead to interaction-associated symptoms. However, we could not identify a database specializing in DDI for pediatrics. Previous studies did not find an age-related trend in the magnitude of DDIs, although it should be noted that there are insufficient data for children under 2 years of age [ 32 , 33 ]. In addition, extrapolating data from adults to children may over- or underestimate the severity of DDIs [ 34 ]. Additionally, as most databases are limited to the information on the interactions of two drugs, potential synergistic or antagonistic effects of combinations consisting of three or more drugs might be overlooked.

Furthermore, the allowed maximum time interval of 24 h between the administration of two drugs may be too long for an interaction for some drug pairs. According to a previous review by Bakker et al., the optimal time interval would consider the half-lives of interacting drugs [ 21 ]. However, due to the developmental variability of pharmacokinetics and pharmacodynamics in children, it is very challenging to determine standardized drug half-lives in the pediatric population [ 35 ]. In addition, the individual patients’ conditions, such as renal function, can also have significant influence on drugs’ half-lives [ 36 ]. In addition, a constant plasma concentration is aimed for with many drugs, which is why a longer-lasting interaction potential can be assumed, although the half-lives of the individual drugs are varying. To ensure a standardized approach for evaluating DDI, we established a 24-h time interval as described in the review by Bakker et al. if consideration of drug half-lives is not feasible [ 21 ]. This methodological approach might potentially increase the risk of overestimation.

The retrospective design is another limitation of this study, as using nurses’ and physicians’ daily documentation entails the risk of missing data. That could lead to information bias, as the documentation was not primarily compiled to answer research questions. Consequently, using the patient documentation as data basis may have an impact on the identification of symptoms themselves, and on the observed associations between interacting drugs pairs and subsequent symptoms. Furthermore, due to the retrospective design, we could not assess whether the physicians accepted certain expectable symptoms as an inevitable consequence of the chosen drug therapy because the patient’s state of health required the administration.

In addition, it should be kept in mind that the administration of a HAM alone and the underlying disease may also increase the risk of adverse events. However, we focused on acknowledged DDIs and interaction-associated symptoms reported in established databases. We endeavored to identify symptoms prone to being associated with a DDI by calculating ORs, as those interactions potentially contribute to evoking symptoms, or to prolonging or exacerbating existing symptoms. These drug combinations should therefore be given special consideration in the routine care of critically ill pediatric patients who are already at risk.

Our study sheds light on a topic about which knowledge is limited: symptoms associated with DDIs involving HAM. We showed that pDDIs involving HAM are very common in pediatric intensive care. More than one in four observed symptoms were associated with a DDI. These symptoms were mainly disturbances of electrolyte and fluid balance and hemodynamic alterations. Focusing on drug pairs with a potentially increased risk of triggering these symptoms, we identified eight specific drug pairs composed of eight different drugs. However, administration of these drug pairs may be unavoidable. In that case, patients should be carefully monitored for electrolyte and fluid balance disturbances and hemodynamic alterations, which were observed as the most frequent interaction-associated symptoms.

Below is the link to the electronic supplementary material.

Acknowledgements

We thank all the physicians and nurses in the participating PICU for their helpful collaboration.

Declarations

Open Access funding enabled and organized by Projekt DEAL.

A. Bertsche reports grants from UCB Pharma GmbH and honoraria for speaking engagements from Biogen GmbH, Desitin Arzneimittel GmbH, Eisai GmbH, GW Pharma GmbH, Neuraxpharm GmbH, Shire/Takeda GmbH, UCB Pharma GmbH, and ViroPharma GmbH. The other authors declare they have no conflicts of interests.

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to ethical and privacy considerations to protect the confidentiality of patients.

The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Ethics Committee of the Medical Faculty, Leipzig University, Germany (127/19-ek). The study was performed in accordance with the ethical standards as laid down in the 1964 Declaration of Helsinki and its later amendments.

As this was a retrospective study and data were collected from patient records without any influence on patients’ treatment, the ethics committee waived informed consent.

Not applicable.

Conceptualization: Lisa Marie Kiesel and Martina Patrizia Neininger; methodology: Lisa Marie Kiesel, Martina Patrizia Neininger, Astrid Bertsche, Thilo Bertsche, Manuela Siekmeyer, and Wieland Kiess; formal analysis: Lisa Marie Kiesel; investigation: Lisa Marie Kiesel and Martina Patrizia Neininger; writing—original draft preparation: Lisa Marie Kiesel and Martina Patrizia Neininger; writing—review and editing: Astrid Bertsche, Thilo Bertsche, Manuela Siekmeyer, and Wieland Kiess; supervision: Martina Patrizia Neininger; project administration: Lisa Marie Kiesel and Martina Patrizia Neininger. All authors read and approved the final version.

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• Volume 58, Issue 17
• Where is the research on sport-related concussion in Olympic athletes? A descriptive report and assessment of the impact of access to multidisciplinary care on recovery
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• http://orcid.org/0000-0002-3298-5719 Thomas Romeas 1 , 2 , 3 ,
• http://orcid.org/0000-0003-1748-7241 Félix Croteau 3 , 4 , 5 ,
• Suzanne Leclerc 3 , 4
• 1 Sport Sciences , Institut national du sport du Québec , Montreal , Quebec , Canada
• 2 School of Optometry , Université de Montréal , Montreal , Quebec , Canada
• 3 IOC Research Centre for Injury Prevention and Protection of Athlete Health , Réseau Francophone Olympique de la Recherche en Médecine du Sport , Montreal , Quebec , Canada
• 4 Sport Medicine , Institut national du sport du Québec , Montreal , Quebec , Canada
• 5 School of Physical and Occupational Therapy , McGill University , Montreal , Quebec , Canada
• Correspondence to Dr Thomas Romeas; thomas.romeas{at}umontreal.ca

Objectives This cohort study reported descriptive statistics in athletes engaged in Summer and Winter Olympic sports who sustained a sport-related concussion (SRC) and assessed the impact of access to multidisciplinary care and injury modifiers on recovery.

Methods 133 athletes formed two subgroups treated in a Canadian sport institute medical clinic: earlier (≤7 days) and late (≥8 days) access. Descriptive sample characteristics were reported and unrestricted return to sport (RTS) was evaluated based on access groups as well as injury modifiers. Correlations were assessed between time to RTS, history of concussions, the number of specialist consults and initial symptoms.

Results 160 SRC (median age 19.1 years; female=86 (54%); male=74 (46%)) were observed with a median (IQR) RTS duration of 34.0 (21.0–63.0) days. Median days to care access was different in the early (1; n SRC =77) and late (20; n SRC =83) groups, resulting in median (IQR) RTS duration of 26.0 (17.0–38.5) and 45.0 (27.5–84.5) days, respectively (p<0.001). Initial symptoms displayed a meaningful correlation with prognosis in this study (p<0.05), and female athletes (52 days (95% CI 42 to 101)) had longer recovery trajectories than male athletes (39 days (95% CI 31 to 65)) in the late access group (p<0.05).

Conclusions Olympic athletes in this cohort experienced an RTS time frame of about a month, partly due to limited access to multidisciplinary care and resources. Earlier access to care shortened the RTS delay. Greater initial symptoms and female sex in the late access group were meaningful modifiers of a longer RTS.

• Brain Concussion
• Cohort Studies
• Retrospective Studies

Data availability statement

Data are available on reasonable request. Due to the confidential nature of the dataset, it will be shared through a controlled access repository and made available on specific and reasonable requests.

https://doi.org/10.1136/bjsports-2024-108211

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WHAT IS ALREADY KNOWN ON THIS TOPIC

Most data regarding the impact of sport-related concussion (SRC) guidelines on return to sport (RTS) are derived from collegiate or recreational athletes. In these groups, time to RTS has steadily increased in the literature since 2005, coinciding with the evolution of RTS guidelines. However, current evidence suggests that earlier access to care may accelerate recovery and RTS time frames.

WHAT THIS STUDY ADDS

This study reports epidemiological data on the occurrence of SRC in athletes from several Summer and Winter Olympic sports with either early or late access to multidisciplinary care. We found the median time to RTS for Olympic athletes with an SRC was 34.0 days which is longer than that reported in other athletic groups such as professional or collegiate athletes. Time to RTS was reduced by prompt access to multidisciplinary care following SRC, and sex-influenced recovery in the late access group with female athletes having a longer RTS timeline. Greater initial symptoms, but not prior concussion history, were also associated with a longer time to RTS.

HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY

Considerable differences exist in access to care for athletes engaged in Olympic sports, which impact their recovery. In this cohort, several concussions occurred during international competitions where athletes are confronted with poor access to organised healthcare. Pathways for prompt access to multidisciplinary care should be considered by healthcare authorities, especially for athletes who travel internationally and may not have the guidance or financial resources to access recommended care.

Introduction

After two decades of consensus statements, sport-related concussion (SRC) remains a high focus of research, with incidence ranging from 0.1 to 21.5 SRC per 1000 athlete exposures, varying according to age, sex, sport and level of competition. 1 2 Evidence-based guidelines have been proposed by experts to improve its identification and management, such as those from the Concussion in Sport Group. 3 Notably, they recommend specific strategies to improve SRC detection and monitoring such as immediate removal, 4 prompt access to healthcare providers, 5 evidence-based interventions 6 and multidisciplinary team approaches. 7 It is believed that these guidelines contribute to improving the early identification and management of athletes with an SRC, thereby potentially mitigating its long-term consequences.

Nevertheless, evidence regarding the impact of SRC guidelines implementation remains remarkably limited, especially within high-performance sport domains. In fact, most reported SRC data focus on adolescent student-athletes, collegiate and sometimes professional athletes in the USA but often neglect Olympians. 1 2 8–11 Athletes engaged in Olympic sports, often referred to as elite amateurs, are typically classified among the highest performers in elite sport, alongside professional athletes. 12 13 They train year-round and uniquely compete regularly on the international stage in sports that often lack professional leagues and rely on highly variable resources and facilities, mostly dependent on winning medals. 14 Unlike professional athletes, Olympians do not have access to large financial rewards. Although some Olympians work or study in addition to their intensive sports practice, they can devote more time to full-time sports practice compared with collegiate athletes. Competition calendars in Olympians differ from collegiate athletes, with periodic international competitions (eg, World Cups, World Championships) throughout the whole year rather than regular domestic competitions within a shorter season (eg, semester). Olympians outclass most collegiate athletes, and only the best collegiate athletes will have the chance to become Olympians and/or professionals. 12 13 15 In Canada, a primary reason for limited SRC data in Olympic sports is that the Canadian Olympic and Paralympic Sports Institute (COPSI) network only adopted official guidelines in 2018 to standardise care for athletes’ SRC nationwide. 16 17 The second reason could be the absence of a centralised medical structure and surveillance systems, identified as key factors contributing to the under-reporting and underdiagnosis of athletes with an SRC. 18

Among the available evidence on the evolution of SRC management, a 2023 systematic review and meta-analysis in athletic populations including children, adolescents and adults indicated that a full return to sport (RTS) could take up to a month but is estimated to require 19.8 days on average (15.4 days in adults), as opposed to the initial expectation of approximately 10.0 days based on studies published prior to 2005. 19 In comparison, studies focusing strictly on American collegiate athletes report median times to RTS of 16 days. 9 20 21 Notably, a recent study of military cadets reported an even longer return to duty times of 29.4 days on average, attributed to poorer access to care and fewer incentives to return to play compared with elite sports. 22 In addition, several modifiers have also been identified as influencing the time to RTS, such as the history of concussions, type of sport, sex, past medical problems (eg, preinjury modifiers), as well as the initial number of symptoms and their severity (eg, postinjury modifiers). 20 22 The evidence regarding the potential influence of sex on the time to RTS has yielded mixed findings in this area. 23–25 In fact, females are typically under-represented in SRC research, highlighting the need for additional studies that incorporate more balanced sample representation across sexes and control for known sources of bias. 26 Interestingly, a recent Concussion Assessment, Research and Education Consortium study, which included a high representation of concussed female athletes (615 out of 1071 patients), revealed no meaningful differences in RTS between females and males (13.5 and 11.8 days, respectively). 27 Importantly, findings in the sporting population suggested that earlier initiation of clinical care is linked to shorter recovery after concussion. 5 28 However, these factors affecting the time to RTS require a more thorough investigation, especially among athletes engaged in Olympic sports who may or may not have equal access to prompt, high-quality care.

Therefore, the primary objective of this study was to provide descriptive statistics among athletes with SRC engaged in both Summer and Winter Olympic sport programmes over a quadrennial, and to assess the influence of recommended guidelines of the COPSI network and the fifth International Consensus Conference on Concussion in Sport on the duration of RTS performance. 16 17 Building on available evidence, the international schedule constraints, variability in resources 14 and high-performance expectation among this elite population, 22 prolonged durations for RTS, compared with what is typically reported (eg, 16.0 or 15.4 days), were hypothesised in Olympians. 3 19 The secondary objective was to more specifically evaluate the impact of access to multidisciplinary care and injury modifiers on the time to RTS. Based on current evidence, 5 7 29 30 the hypothesis was formulated that athletes with earlier multidisciplinary access would experience a faster RTS. Regarding injury modifiers, it was expected that female and male athletes would show similar time to RTS despite presenting sex-specific characteristics of SRC. 31 The history of concussions, the severity of initial symptoms and the number of specialist consults were expected to be positively correlated to the time to RTS. 20 32

Participants

A total of 133 athletes (F=72; M=61; mean age±SD: 20.7±4.9 years old) who received medical care at the Institut national du sport du Québec, a COPSI training centre set up with a medical clinic, were included in this cohort study with retrospective analysis. They participated in 23 different Summer and Winter Olympic sports which were classified into six categories: team (soccer, water polo), middle distance/power (rowing, swimming), speed/strength (alpine skiing, para alpine skiing, short and long track speed skating), precision/skill-dependent (artistic swimming, diving, equestrian, figure skating, gymnastics, skateboard, synchronised skating, trampoline) and combat/weight-making (boxing, fencing, judo, para judo, karate, para taekwondo, wrestling) sports. 13 This sample consists of two distinct groups: (1) early access group in which athletes had access to a medical integrated support team of multidisciplinary experts within 7 days following their SRC and (2) late access group composed of athletes who had access to a medical integrated support team of multidisciplinary experts eight or more days following their SRC. 5 30 Inclusion criteria for the study were participation in a national or international-level sports programme 13 and having sustained at least one SRC diagnosed by an authorised healthcare practitioner (eg, physician and/or physiotherapist).

Clinical context

The institute clinic provides multidisciplinary services for care of patients with SRC including a broad range of recommended tests for concussion monitoring ( table 1 ). The typical pathway for the athletes consisted of an initial visit to either a sports medicine physician or their team sports therapist. A clinical diagnosis of SRC was then confirmed by a sports medicine physician, and referral for the required multidisciplinary assessments ensued based on the patient’s signs and symptoms. Rehabilitation progression was based on the evaluation of exercise tolerance, 33 priority to return to cognitive tasks and additional targeted support based on clinical findings of a cervical, visual or vestibular nature. 17 The expert team worked in an integrated manner with the athlete and their coaching staff for the rehabilitation phase, including regular round tables and ongoing communication. 34 For some athletes, access to recommended care was fee based, without a priori agreements with a third party payer (eg, National Sports Federation).

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Main evaluations performed to guide the return to sport following sport-related concussion

Data collection

Data were collected at the medical clinic using a standardised injury surveillance form based on International Olympic Committee guidelines. 35 All injury characteristics were extracted from the central injury database between 1 July 2018 and 31 July 2022. This period corresponds to a Winter Olympic sports quadrennial but also covers 3 years for Summer Olympic sports due to the postponing of the Tokyo 2020 Olympic Games. Therefore, the observation period includes a typical volume of competitions across sports and minimises differences in exposure based on major sports competition schedules. The information extracted from the database included: participant ID, sex, date of birth, sport, date of injury, type of injury, date of their visit at the clinic, clearance date of unrestricted RTS (eg, defined as step 6 of the RTS strategy with a return to normal gameplay including competitions), the number and type of specialist consults, mechanism of injury (eg, fall, hit), environment where the injury took place (eg, training, competition), history of concussions, history of modifiers (eg, previous head injury, migraines, learning disability, attention deficit disorder or attention deficit/hyperactivity disorder, depression, anxiety, psychotic disorder), as well as the number of symptoms and the total severity score from the first Sport Concussion Assessment Tool 5 (SCAT5) assessment following SRC. 17

Following a Shapiro-Wilk test, medians, IQR and non-parametric tests were used for the analyses because of the absence of normal distributions for all the variables in the dataset (all p<0.001). The skewness was introduced by the presence of individuals that required lengthy recovery periods. One participant was removed from the analysis because their time to consult with the multidisciplinary team was extremely delayed (>1 year).

Descriptive statistics were used to describe the participant’s demographics, SRC characteristics and risk factors in the total sample. Estimated incidences of SRC were also reported for seven resident sports at the institute for which it was possible to quantify a detailed estimate of training volume based on the annual number of training and competition hours as well as the number of athletes in each sport.

To assess if access to multidisciplinary care modified the time to RTS, we compared time to RTS between early and late access groups using a method based on median differences described elsewhere. 36 Wilcoxon rank sum tests were also performed to make between-group comparisons on single variables of age, time to first consult, the number of specialists consulted and medical visits. Fisher’s exact tests were used to compare count data between groups on variables of sex, history of concussion, time since the previous concussion, presence of injury modifiers, environment and mechanism of injury. Bonferroni corrections were applied for multiple comparisons in case of meaningful differences.

To assess if injury modifiers modified time to RTS in the total sample, we compared time to RTS between sexes, history of concussions, time since previous concussion or other injury modifiers using a method based on median differences described elsewhere. 36 Kaplan-Meier curves were drawn to illustrate time to RTS differences between sexes (origin and start time: date of injury; end time: clearance date of unrestricted RTS). Trajectories were then assessed for statistical differences using Cox proportional hazards model. Wilcoxon rank sum tests were employed for comparing the total number of symptoms and severity scores on the SCAT5. The association of multilevel variables on return to play duration was evaluated in the total sample with Kruskal-Wallis rank tests for environment, mechanism of injury, history of concussions and time since previous concussion. For all subsequent analyses of correlations between SCAT5 results and secondary variables, only data obtained from SCAT5 assessments within the acute phase of injury (≤72 hours) were considered (n=65 SRC episodes in the early access group). 37 Spearman rank correlations were estimated between RTS duration, history of concussions, number of specialist consults and total number of SCAT5 symptoms or total symptom severity. All statistical tests were performed using RStudio (R V.4.1.0, The R Foundation for Statistical Computing). The significance level was set to p<0.05.

Equity, diversity and inclusion statement

The study population is representative of the Canadian athletic population in terms of age, gender, demographics and includes a balanced representation of female and male athletes. The study team consists of investigators from different disciplines and countries, but with a predominantly white composition and under-representation of other ethnic groups. Our study population encompasses data from the Institut national du sport du Québec, covering individuals of all genders, ethnicities and geographical regions across Canada.

Patient and public involvement

The patients or the public were not involved in the design, conduct, reporting or dissemination plans of our research.

Sample characteristics

During the 4-year period covered by this retrospective chart review, a total of 160 SRC episodes were recorded in 132 athletes with a median (IQR) age of 19.1 (17.8–22.2) years old ( table 2 ). 13 female and 10 male athletes had multiple SRC episodes during this time. The sample had a relatively balanced number of females (53.8%) and males (46.2%) with SRC included. 60% of the sample reported a history of concussion, with 35.0% reporting having experienced more than two episodes. However, most of these concussions had occurred more than 1 year before the SRC for which they were being treated. Within this sample, 33.1% of participants reported a history of injury modifiers. Importantly, the median (IQR) time to first clinic consult was 10.0 (1.0–20.0) days and the median (IQR) time to RTS was 34.0 (21.0–63.0) days in this sample ( table 3 ). The majority of SRCs occurred during training (56.3%) rather than competition (33.1%) and were mainly due to a fall (63.7%) or a hit (31.3%). The median (IQR) number of follow-up consultations and specialists consulted after the SRC were, respectively, 9 (5.0–14.3) and 3 (2.0–4.0).

Participants demographics

Sport-related concussion characteristics

Among seven sports of the total sample (n=89 SRC), the estimated incidence of athletes with SRC was highest in short-track speed skating (0.47/1000 hours; 95% CI 0.3 to 0.6), and lower in boxing, trampoline, water polo, judo, artistic swimming, and diving (0.24 (95% CI 0.0 to 0.5), 0.16 (95% CI 0.0 to 0.5), 0.13 (95% CI 0.1 to 0.2), 0.11 (95% CI 0.1 to 0.2), 0.09 (95% CI 0.0 to 0.2) and 0.06 (95% CI 0.0 to 0.1)/1000, respectively ( online supplemental material ). Furthermore, most athletes sustained an SRC in training (66.5%; 95% CI 41.0 to 92.0) rather than competition (26.0%; 95% CI 0.0 to 55.0) except for judo athletes (20.0% (95% CI 4.1 to 62.0) and 80.0% (95% CI 38.0 to 96.0), respectively). Falls were the most common injury mechanism in speed skating, trampoline and judo while hits were the most common injury mechanism in boxing, water polo, artistic swimming and diving.

Supplemental material

Access to care.

The median difference in time to RTS was 19 days (95% CI 9.3 to 28.7; p<0.001) between the early (26 (IQR 17.0–38.5) days) and late (45 (IQR 27.5–84.5) days) access groups ( table 3 ; figure 1 ). Importantly, the distribution of SRC environments was different between both groups (p=0.008). The post hoc analysis demonstrated a meaningful difference in the distribution of SRC in training and competition environments between groups (p=0.029) but not for the other comparisons. There was a meaningful difference between the groups in time to first consult (p<0.001; 95% CI −23.0 to −15.0), but no meaningful differences between groups in median age (p=0.176; 95% CI −0.3 to 1.6), sex distribution (p=0.341; 95% CI 0.7 to 2.8), concussion history (p=0.210), time since last concussion (p=0.866), mechanisms of SRC (p=0.412), the presence of modifiers (p=0.313; 95% CI 0.3 to 1.4) and the number of consulted specialists (p=0.368; 95% CI −5.4 to 1.0) or medical visits (p=0.162; 95% CI −1.0 to 3.0).

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Time to return to sport following sport-related concussion as a function of group’s access to care and sex. Outliers: below=Q1−1.5×IQR; above=Q3+1.5×IQR.

The median difference in time to RTS was 6.5 days (95% CI −19.3 to 5.3; p=0.263; figure 1 ) between female (37.5 (IQR 22.0–65.3) days) and male (31.0 (IQR 20.0–48.0) days) athletes. Survival analyses highlighted an increased hazard of longer recovery trajectory in female compared with male athletes (HR 1.4; 95% CI 1.4 to 0.7; p=0.052; figure 2A ), which was mainly driven by the late (HR 1.8; 95% CI 1.8 to 0.6; p=0.019; figure 2C ) rather than the early (HR 1.1; 95% CI 1.1 to 0.9; p=0.700; figure 2B ) access group. Interestingly, a greater number of female athletes (n=15) required longer than 100 days for RTS as opposed to the male athletes (n=6). There were no meaningful differences between sexes for the total number of symptoms recorded on the SCAT5 (p=0.539; 95% CI −1.0 to 2.0) nor the total symptoms total severity score (p=0.989; 95% CI −5.0 to 5.0).

Time analysis of sex differences in the time to return to sport following sport-related concussion in the (A) total sample, as well as (B) early, and (C) late groups using survival curves with 95% confidence bands and tables of time-specific number of patients at risk (censoring proportion: 0%).

History of modifiers

SRC modifiers are presented in table 2 , and their influence on RTP is shown in table 4 . The median difference in time to RTS was 1.5 days (95% CI −10.6 to 13.6; p=0.807) between athletes with none and one episode of previous concussion, was 3.5 days (95% CI −13.9 to 19.9; p=0.728) between athletes with none and two or more episodes of previous concussion, and was 2 days (95% CI −12.4 to 15.4; p=0.832) between athletes with one and two or more episodes of previous concussion. The history of concussions (none, one, two or more) had no meaningful impact on the time to RTS (p=0.471). The median difference in time to RTS was 4.5 days (95% CI −21.0 to 30.0; p=0.729) between athletes with none and one episode of concussion in the previous year, was 2 days (95% CI −10.0 to 14.0; p=0.744) between athletes with none and one episode of concussion more than 1 year ago, and was 2.5 days (95% CI −27.7 to 22.7; p=0.846) between athletes with an episode of concussion in the previous year and more than 1 year ago. Time since the most recent concussion did not change the time to RTS (p=0.740). The longest time to RTS was observed in the late access group in which athletes had a concussion in the previous year, with a very large spread of durations (65.0 (IQR 33.0–116.5) days). The median difference in time to RTS was 3 days (95% CI −13.1 to 7.1; p=0.561) between athletes with and without other injury modifiers. The history of other injury modifiers had no meaningful influence on the time to RTS (95% CI −6.0 to 11.0; p=0.579).

Preinjury modifiers of time to return to sport following SRC

SCAT5 symptoms and severity scores

Positive associations were observed between the time to RTS and the number of initial symptoms (r=0.3; p=0.010; 95% CI 0.1 to 0.5) or initial severity score (r=0.3; p=0.008; 95% CI 0.1 to 0.5) from the SCAT5. The associations were not meaningful between the number of specialist consultations and the initial number of symptoms (r=−0.1; p=0.633; 95% CI −0.3 to 0.2) or initial severity score (r=−0.1; p=0.432; 95% CI −0.3 to 0.2). Anecdotally, most reported symptoms following SRC were ‘headache’ (86.2%) and ‘pressure in the head’ (80.0%), followed by ‘fatigue’ (72.3%), ‘neck pain’ (70.8%) and ‘not feeling right’ (67.7%; online supplemental material ).

This study is the first to report descriptive data on athletes with SRC collected across several sports during an Olympic quadrennial, including athletes who received the most recent evidence-based care at the time of data collection. Primarily, results indicate that the time to RTS in athletes engaged in Summer and Winter Olympic sports may require a median (IQR) of 34.0 (21.0–63.0) days. Importantly, findings demonstrated that athletes with earlier (≤7 days) access to multidisciplinary concussion care showed faster RTS compared with those with late access. Time to RTS exhibited large variability where sex had a meaningful influence on the recovery pathway in the late access group. Initial symptoms, but not history of concussion, were correlated with prognosis in this sample. The main reported symptoms were consistent with previous studies. 38 39

Time to RTS in Olympic sports

This study provides descriptive data on the impact of SRC monitoring programmes on recovery in elite athletes engaged in Olympic sports. As hypothesised, the median time to RTS found in this study (eg, 34.0 days) was about three times longer than those found in reports from before 2005, and 2 weeks longer than the typical median values (eg, 19.8 days) recently reported in athletic levels including youth (high heterogeneity, I 2 =99.3%). 19 These durations were also twice as long as the median unrestricted time to RTS observed among American collegiate athletes, which averages around 16 days. 9 20 21 However, they were more closely aligned with findings from collegiate athletes with slow recovery (eg, 34.7 days) and evidence from military cadets with poor access where return to duty duration was 29.4 days. 8 22 Several reasons could explain such extended time to RTS, but the most likely seems to be related to the diversity in access among these sports to multidisciplinary services (eg, 10.0 median days (1–20)), well beyond the delays experienced by collegiate athletes, for example (eg, 0.0 median days (0–2)). 40 In the total sample, the delays to first consult with the multidisciplinary clinic were notably mediated by the group with late access, whose athletes had more SRC during international competition. One of the issues for athletes engaged in Olympic sports is that they travel abroad year-round for competitions, in contrast with collegiate athletes who compete domestically. These circumstances likely make access to quality care very variable and make the follow-up of care less centralised. Also, access to resources among these sports is highly variable (eg, medal-dependant), 14 and at the discretion of the sport’s leadership (eg, sport federation), who may decide to prioritise more or fewer resources to concussion management considering the relatively low incidence of this injury. Another explanation for the longer recovery times in these athletes could be the lack of financial incentives to return to play faster, which are less prevalent among Olympic sports compared with professionals. However, the stakes of performance and return to play are still very high among these athletes.

Additionally, it is plausible that studies vary their outcome with shifting operational definitions such as resolution of symptoms, return to activities, graduated return to play or unrestricted RTS. 19 40 It is understood that resolution of symptoms may occur much earlier than return to preinjury performance levels. Finally, an aspect that has been little studied to date is the influence of the sport’s demands on the RTS. For example, acrobatic sports requiring precision/technical skills such as figure skating, trampoline and diving, which involve high visuospatial and vestibular demands, 41 might require more time to recover or elicit symptoms for longer times. Anecdotally, athletes who experienced a long time to RTS (>100 days) were mostly from precision/skill-dependent sports in this sample. The sports demand should be further considered as an injury modifier. More epidemiological reports that consider the latest guidelines are therefore necessary to gain a better understanding of the true time to RTS and impact following SRC in Olympians.

Supporting early multidisciplinary access to care

In this study, athletes who obtained early access to multidisciplinary care after SRC recovered faster than those with late access to multidisciplinary care. This result aligns with findings showing that delayed access to a healthcare practitioner delays recovery, 19 including previous evidence in a sample of patients from a sports medicine clinic (ages 12–22), indicating that the group with a delayed first clinical visit (eg, 8–20 days) was associated with a 5.8 times increased likelihood of a recovery longer than 30 days. 5 Prompt multidisciplinary approach for patients with SRC is suggested to yield greater effectiveness over usual care, 3 6 17 which is currently evaluated under randomised controlled trial. 42 Notably, early physical exercise and prescribed exercise (eg, 48 hours postinjury) are effective in improving recovery compared with strict rest or stretching. 43 44 In fact, preclinical and clinical studies have shown that exercise has the potential to improve neurotransmission, neuroplasticity and cerebral blood flow which supports that the physically trained brain enhanced recovery. 45 46 Prompt access to specialised healthcare professionals can be challenging in some contexts (eg, during international travel), and the cost of accessing medical care privately may prove further prohibitive. This barrier to recovery should be a priority for stakeholders in Olympic sports and given more consideration by health authorities.

Estimated incidences and implications

The estimated incidences of SRC were in the lower range compared with what is reported in other elite sport populations. 1 2 However, the burden of injury remained high for these sports, and the financial resources as well as expertise required to facilitate athletes’ rehabilitation was considerable (median number of consultations: 9.0). Notably, the current standard of public healthcare in Canada does not subsidise the level of support recommended following SRC as first-line care, and the financial subsidisation of this recommended care within each federation is highly dependent on the available funding, varying significantly between sports. 14 Therefore, the ongoing efforts to improve education, prevention and early recognition, modification of rules to make the environments safer and multidisciplinary care access for athletes remain crucial. 7

Strength and limitations

This unique study provides multisport characteristics following the evolution of concussion guidelines in Summer and Winter Olympic sports in North America. Notably, it features a balance between the number of female and male athletes, allowing the analysis of sex differences. 23 26 In a previous review of 171 studies informing consensus statements, samples were mostly composed of more than 80% of male participants, and more than 40% of these studies did not include female participants at all. 26 This study also included multiple non-traditional sports typically not encompassed in SRC research, feature previously identified as a key requirement of future epidemiological research. 47

However, it must be acknowledged that potential confounding factors could influence the results. For example, the number of SRC detected during the study period does not account for potentially unreported concussions. Nevertheless, this figure should be minimal because these athletes are supervised both in training and in competition by medical staff. Next, the sport types were heterogeneous, with inconsistent risk for head impacts or inconsistent sport demand which might have an influence on recovery. Furthermore, the number of participants or sex in each sport was not evenly distributed, with short-track speed skaters representing a large portion of the overall sample (32.5%), for example. Additionally, the number of participants with specific modifiers was too small in the current sample to conclude whether the presence of precise characteristics (eg, history of concussion) impacted the time to RTS. Also, the group with late access was more likely to consist of athletes who sought specialised care for persistent symptoms. These complex cases are often expected to require additional time to recover. 48 Furthermore, athletes in the late group may have sought support outside of the institute medical clinic, without a coordinated multidisciplinary approach. Therefore, the estimation of clinical consultations was tentative for this group and may represent a potential confounding factor in this study.

This is the first study to provide evidence of the prevalence of athletes with SRC and modifiers of recovery in both female and male elite-level athletes across a variety of Summer and Winter Olympic sports. There was a high variability in access to care in this group, and the median (IQR) time to RTS following SRC was 34.0 (21.0–63.0) days. Athletes with earlier access to multidisciplinary care took nearly half the time to RTS compared with those with late access. Sex had a meaningful influence on the recovery pathway in the late access group. Initial symptom number and severity score but not history of concussion were meaningful modifiers of recovery. Injury surveillance programmes targeting national sport organisations should be prioritised to help evaluate the efficacy of recommended injury monitoring programmes and to help athletes engaged in Olympic sports who travel a lot internationally have better access to care. 35 49

Ethics statements

Patient consent for publication.

Not applicable.

Ethics approval

This study involves human participants and was approved by the ethics board of Université de Montréal (certificate #2023-4052). Participants gave informed consent to participate in the study before taking part.

Acknowledgments

The authors would like to thank the members of the concussion interdisciplinary clinic of the Institut national du sport du Québec for collecting the data and for their unconditional support to the athletes.

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Supplementary materials

Supplementary data.

This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.

• Data supplement 1

X @ThomasRomeas

Correction notice This article has been corrected since it published Online First. The ORCID details have been added for Dr Croteau.

Contributors TR, FC and SL were involved in planning, conducting and reporting the work. François Bieuzen and Magdalena Wojtowicz critically reviewed the manuscript. TR is guarantor.

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Dynamic load acquisition method for a crawler driving structure of a roadheader robot under random road

• Original Article
• Published: 05 September 2024

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• Yang Liu 1 ,
• Hong Zhang 1 ,
• Yang Song 2 ,
• Xiaobing Chen 1 &
• Guozhu Yin 1  

A method utilizing an embedded sensing system to determine the dynamic load is proposed to address the problem of not being able to directly obtain the dynamic load time history of a crawler driving structure in the harsh underground working environment of a coal mine roadheader robot. A crawler driving structure and its embedded sensing system are designed. A rigid–flexible coupling simulation model of the roadheader robot is established, and the dynamic load time history parameters of the crawler obtained from the simulation and test data are compared and analyzed. Results show that the load fluctuation of the tested track plate at the idler and sprocket is significant. By contrast, the fluctuation of the free segment is minimal. The load fluctuation range of the support segment of the hard road is −5–15 MPa, and that of the soft sand road is −10–25 MPa. Moreover, the track plate exhibits noticeable warping and winding phenomenon relative to the road wheel on the soft sand road. The changing trend of the crawler dynamic load time history obtained from simulation and test is relatively consistent. This study provides a reference for the crawler driving structure’s structural optimization and reliability research.

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Abbreviations

Working tension

Tension of the free segment

Tension of the working segment

Dynamic forces by the left side of the i th road wheel

Dynamic forces by the right sides of the i th road wheel

Supporting force on the ground facing the i th road wheel

Vertical component of the supporting force of the vehicle body on the i th road wheel

Sprocket torque

Young’s modulus of the track

Radius of the dividing circle of the sprocket

Diameter of the ith road wheel

Diameters of the idler

Diameters of the sprocket

Track overhang

Relative vertical of the track

Track length

Thickness of the track

Angle between F Ti and the horizontal road surface

Angle between F T(i+1) and the horizontal road surface

Radius of the ith road wheel

Rotational inertia of the i th road wheel

Rotational angular velocity of the i th road wheel

Friction coefficient between the track shoe and the ground

Mass of the i th road wheel

Stiffness index of contact

Damping index of contact

Dent index of contact

Normal contact

Contact stiffness coefficient

Damping coefficient

Contact penetration depth

Track mass per unit length

Relative speed of the track link relative to the vehicle

Cross-sectional area of deformation of tested track plate

Dynamic load of the tested track plate in the free segment

Dynamic load of the tested track plate in the working segment

Dynamic load between the i th road wheel and the (i+1) th road wheel

Bend stress when the tested track plate wrapped around the i th road wheel

Bend stress when the tested track plate wrapped around the idler

Bend stress when the tested track plate wrapped around the sprocket

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Acknowledgments

This work is supported by the National Natural Science Foundation of China (grant number 52075355) and Shanxi Provincial Key Research and Development Project (grant number 202202100401012).

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Authors and affiliations.

School of Mechanical Engineering, Taiyuan University of Science and Technology, Taiyuan, Shanxi, People’s Republic of China

Yang Liu, Hong Zhang, Xiaobing Chen & Guozhu Yin

Taiyuan Research Institute Company Limited, China Coal Technology and Engineering Group, Taiyuan, Shanxi, People’s Republic of China

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Corresponding author

Correspondence to Hong Zhang .

Additional information

Yang Liu is currently a master’s candidate student in Mechanical Engineering at the Taiyuan University of Science and Technology, Taiyuan, China. His research interests include mechanical condition health monitoring and intelligent maintenance.

Hong Zhang is a Professor at Taiyuan University of Science and Technology, Taiyuan, China. His research interests include machinery and special vehicle dynamics, coal mining equipment development, fluid transmission and control, dynamic monitoring, and fault diagnosis.

Yang Song is currently a mechanical engineer at Taiyuan Research Institute Company Limited, China Coal Technology and Engineering Group. His research interests include continuous carrier equipment and its system theory.

Xiaobing Chen is currently a master’s candidate student in Mechanical Engineering at the Taiyuan University of Science and Technology, Taiyuan, China. His research interests include crawler dynamics and vibration control.

Guozhu Yin is currently a master candidate student in Mechanical Engineering at the Taiyuan University of Science and Technology, Taiyuan, China. His research interests include vibration testing and fault diagnosis.

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Liu, Y., Zhang, H., Song, Y. et al. Dynamic load acquisition method for a crawler driving structure of a roadheader robot under random road. J Mech Sci Technol (2024). https://doi.org/10.1007/s12206-024-0814-5

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Received : 31 March 2024

Revised : 20 May 2024

Accepted : 25 May 2024

Published : 05 September 2024

DOI : https://doi.org/10.1007/s12206-024-0814-5

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