Research methodology vs. research methods
The research methodology or design is the overall strategy and rationale that you used to carry out the research. Whereas, research methods are the specific tools and processes you use to gather and understand the data you need to test your hypothesis.
To further understand research methodology, let’s explore some examples of research methodology:
a. Qualitative research methodology example: A study exploring the impact of author branding on author popularity might utilize in-depth interviews to gather personal experiences and perspectives.
b. Quantitative research methodology example: A research project investigating the effects of a book promotion technique on book sales could employ a statistical analysis of profit margins and sales before and after the implementation of the method.
c. Mixed-Methods research methodology example: A study examining the relationship between social media use and academic performance might combine both qualitative and quantitative approaches. It could include surveys to quantitatively assess the frequency of social media usage and its correlation with grades, alongside focus groups or interviews to qualitatively explore students’ perceptions and experiences regarding how social media affects their study habits and academic engagement.
These examples highlight the meaning of methodology in research and how it guides the research process, from data collection to analysis, ensuring the study’s objectives are met efficiently.
When it comes to writing your study, the methodology in research papers or a dissertation plays a pivotal role. A well-crafted methodology section of a research paper or thesis not only enhances the credibility of your research but also provides a roadmap for others to replicate or build upon your work.
Wondering how to write the research methodology section? Follow these steps to create a strong methods chapter:
At the start of a research paper , you would have provided the background of your research and stated your hypothesis or research problem. In this section, you will elaborate on your research strategy.
Begin by restating your research question and proceed to explain what type of research you opted for to test it. Depending on your research, here are some questions you can consider:
a. Did you use qualitative or quantitative data to test the hypothesis?
b. Did you perform an experiment where you collected data or are you writing a dissertation that is descriptive/theoretical without data collection?
c. Did you use primary data that you collected or analyze secondary research data or existing data as part of your study?
These questions will help you establish the rationale for your study on a broader level, which you will follow by elaborating on the specific methods you used to collect and understand your data.
Now that you have told your reader what type of research you’ve undertaken for the dissertation, it’s time to dig into specifics. State what specific methods you used and explain the conditions and variables involved. Explain what the theoretical framework behind the method was, what samples you used for testing it, and what tools and materials you used to collect the data.
Once you have explained the data collection process, explain how you analyzed and studied the data. Here, your focus is simply to explain the methods of analysis rather than the results of the study.
Here are some questions you can answer at this stage:
a. What tools or software did you use to analyze your results?
b. What parameters or variables did you consider while understanding and studying the data you’ve collected?
c. Was your analysis based on a theoretical framework?
Your mode of analysis will change depending on whether you used a quantitative or qualitative research methodology in your study. If you’re working within the hard sciences or physical sciences, you are likely to use a quantitative research methodology (relying on numbers and hard data). If you’re doing a qualitative study, in the social sciences or humanities, your analysis may rely on understanding language and socio-political contexts around your topic. This is why it’s important to establish what kind of study you’re undertaking at the onset.
Now that you have gone through your research process in detail, you’ll also have to make a case for it. Justify your choice of methodology and methods, explaining why it is the best choice for your research question. This is especially important if you have chosen an unconventional approach or you’ve simply chosen to study an existing research problem from a different perspective. Compare it with other methodologies, especially ones attempted by previous researchers, and discuss what contributions using your methodology makes.
No matter how thorough a methodology is, it doesn’t come without its hurdles. This is a natural part of scientific research that is important to document so that your peers and future researchers are aware of it. Writing in a research paper about this aspect of your research process also tells your evaluator that you have actively worked to overcome the pitfalls that came your way and you have refined the research process.
1. Remember who you are writing for. Keeping sight of the reader/evaluator will help you know what to elaborate on and what information they are already likely to have. You’re condensing months’ work of research in just a few pages, so you should omit basic definitions and information about general phenomena people already know.
2. Do not give an overly elaborate explanation of every single condition in your study.
3. Skip details and findings irrelevant to the results.
4. Cite references that back your claim and choice of methodology.
5. Consistently emphasize the relationship between your research question and the methodology you adopted to study it.
To sum it up, what is methodology in research? It’s the blueprint of your research, essential for ensuring that your study is systematic, rigorous, and credible. Whether your focus is on qualitative research methodology, quantitative research methodology, or a combination of both, understanding and clearly defining your methodology is key to the success of your research.
Once you write the research methodology and complete writing the entire research paper, the next step is to edit your paper. As experts in research paper editing and proofreading services , we’d love to help you perfect your paper!
Here are some other articles that you might find useful:
What does research methodology mean, what types of research methodologies are there, what is qualitative research methodology, how to determine sample size in research methodology, what is action research methodology.
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This is very simplified and direct. Very helpful to understand the research methodology section of a dissertation
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Research methods are specific procedures for collecting and analysing data. Developing your research methods is an integral part of your research design . When planning your methods, there are two key decisions you will make.
First, decide how you will collect data . Your methods depend on what type of data you need to answer your research question :
Second, decide how you will analyse the data .
Methods for collecting data, examples of data collection methods, methods for analysing data, examples of data analysis methods, frequently asked questions about methodology.
Data are the information that you collect for the purposes of answering your research question . The type of data you need depends on the aims of your research.
Your choice of qualitative or quantitative data collection depends on the type of knowledge you want to develop.
For questions about ideas, experiences and meanings, or to study something that can’t be described numerically, collect qualitative data .
If you want to develop a more mechanistic understanding of a topic, or your research involves hypothesis testing , collect quantitative data .
Qualitative | ||
---|---|---|
Quantitative | . |
You can also take a mixed methods approach, where you use both qualitative and quantitative research methods.
Primary data are any original information that you collect for the purposes of answering your research question (e.g. through surveys , observations and experiments ). Secondary data are information that has already been collected by other researchers (e.g. in a government census or previous scientific studies).
If you are exploring a novel research question, you’ll probably need to collect primary data. But if you want to synthesise existing knowledge, analyse historical trends, or identify patterns on a large scale, secondary data might be a better choice.
Primary | ||
---|---|---|
Secondary |
In descriptive research , you collect data about your study subject without intervening. The validity of your research will depend on your sampling method .
In experimental research , you systematically intervene in a process and measure the outcome. The validity of your research will depend on your experimental design .
To conduct an experiment, you need to be able to vary your independent variable , precisely measure your dependent variable, and control for confounding variables . If it’s practically and ethically possible, this method is the best choice for answering questions about cause and effect.
Descriptive | ||
---|---|---|
Experimental |
Research method | Primary or secondary? | Qualitative or quantitative? | When to use |
---|---|---|---|
Primary | Quantitative | To test cause-and-effect relationships. | |
Primary | Quantitative | To understand general characteristics of a population. | |
Interview/focus group | Primary | Qualitative | To gain more in-depth understanding of a topic. |
Observation | Primary | Either | To understand how something occurs in its natural setting. |
Secondary | Either | To situate your research in an existing body of work, or to evaluate trends within a research topic. | |
Either | Either | To gain an in-depth understanding of a specific group or context, or when you don’t have the resources for a large study. |
Your data analysis methods will depend on the type of data you collect and how you prepare them for analysis.
Data can often be analysed both quantitatively and qualitatively. For example, survey responses could be analysed qualitatively by studying the meanings of responses or quantitatively by studying the frequencies of responses.
Qualitative analysis is used to understand words, ideas, and experiences. You can use it to interpret data that were collected:
Qualitative analysis tends to be quite flexible and relies on the researcher’s judgement, so you have to reflect carefully on your choices and assumptions.
Quantitative analysis uses numbers and statistics to understand frequencies, averages and correlations (in descriptive studies) or cause-and-effect relationships (in experiments).
You can use quantitative analysis to interpret data that were collected either:
Because the data are collected and analysed in a statistically valid way, the results of quantitative analysis can be easily standardised and shared among researchers.
Research method | Qualitative or quantitative? | When to use |
---|---|---|
Quantitative | To analyse data collected in a statistically valid manner (e.g. from experiments, surveys, and observations). | |
Meta-analysis | Quantitative | To statistically analyse the results of a large collection of studies. Can only be applied to studies that collected data in a statistically valid manner. |
Qualitative | To analyse data collected from interviews, focus groups or textual sources. To understand general themes in the data and how they are communicated. | |
Either | To analyse large volumes of textual or visual data collected from surveys, literature reviews, or other sources. Can be quantitative (i.e. frequencies of words) or qualitative (i.e. meanings of words). |
Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.
Quantitative methods allow you to test a hypothesis by systematically collecting and analysing data, while qualitative methods allow you to explore ideas and experiences in depth.
In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .
A sample is a subset of individuals from a larger population. Sampling means selecting the group that you will actually collect data from in your research.
For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.
Statistical sampling allows you to test a hypothesis about the characteristics of a population. There are various sampling methods you can use to ensure that your sample is representative of the population as a whole.
The research methods you use depend on the type of data you need to answer your research question .
Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.
Methods are the specific tools and procedures you use to collect and analyse data (e.g. experiments, surveys , and statistical tests ).
In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .
In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.
More interesting articles.
Use the links below to jump directly to any section of this guide:
How to choose a topic or question, how to create a working hypothesis or thesis, common research paper methodologies, how to gather and organize evidence , how to write an outline for your research paper, how to write a rough draft, how to revise your draft, how to produce a final draft, resources for teachers .
It is not fair to say that no one writes anymore. Just about everyone writes text messages, brief emails, or social media posts every single day. Yet, most people don't have a lot of practice with the formal, organized writing required for a good academic research paper. This guide contains links to a variety of resources that can help demystify the process. Some of these resources are intended for teachers; they contain exercises, activities, and teaching strategies. Other resources are intended for direct use by students who are struggling to write papers, or are looking for tips to make the process go more smoothly.
The resources in this section are designed to help students understand the different types of research papers, the general research process, and how to manage their time. Below, you'll find links from university writing centers, the trusted Purdue Online Writing Lab, and more.
"Genre and the Research Paper" (Purdue OWL)
There are different types of research papers. Different types of scholarly questions will lend themselves to one format or another. This is a brief introduction to the two main genres of research paper: analytic and argumentative.
"7 Most Popular Types of Research Papers" (Personal-writer.com)
This resource discusses formats that high school students commonly encounter, such as the compare and contrast essay and the definitional essay. Please note that the inclusion of this link is not an endorsement of this company's paid service.
Teachers can give their students a step-by-step guide like these to help them understand the different steps of the research paper process. These guides can be combined with the time management tools in the next subsection to help students come up with customized calendars for completing their papers.
"Ten Steps for Writing Research Papers" (American University)
This resource from American University is a comprehensive guide to the research paper writing process, and includes examples of proper research questions and thesis topics.
"Steps in Writing a Research Paper" (SUNY Empire State College)
This guide breaks the research paper process into 11 steps. Each "step" links to a separate page, which describes the work entailed in completing it.
The links below will help students determine how much time is necessary to complete a paper. If your sources are not available online or at your local library, you'll need to leave extra time for the Interlibrary Loan process. Remember that, even if you do not need to consult secondary sources, you'll still need to leave yourself ample time to organize your thoughts.
"Research Paper Planner: Timeline" (Baylor University)
This interactive resource from Baylor University creates a suggested writing schedule based on how much time a student has to work on the assignment.
"Research Paper Planner" (UCLA)
UCLA's library offers this step-by-step guide to the research paper writing process, which also includes a suggested planning calendar.
There's a reason teachers spend a long time talking about choosing a good topic. Without a good topic and a well-formulated research question, it is almost impossible to write a clear and organized paper. The resources below will help you generate ideas and formulate precise questions.
"How to Select a Research Topic" (Univ. of Michigan-Flint)
This resource is designed for college students who are struggling to come up with an appropriate topic. A student who uses this resource and still feels unsure about his or her topic should consult the course instructor for further personalized assistance.
"25 Interesting Research Paper Topics to Get You Started" (Kibin)
This resource, which is probably most appropriate for high school students, provides a list of specific topics to help get students started. It is broken into subsections, such as "paper topics on local issues."
"Writing a Good Research Question" (Grand Canyon University)
This introduction to research questions includes some embedded videos, as well as links to scholarly articles on research questions. This resource would be most appropriate for teachers who are planning lessons on research paper fundamentals.
"How to Write a Research Question the Right Way" (Kibin)
This student-focused resource provides more detail on writing research questions. The language is accessible, and there are embedded videos and examples of good and bad questions.
It is important to have a rough hypothesis or thesis in mind at the beginning of the research process. People who have a sense of what they want to say will have an easier time sorting through scholarly sources and other information. The key, of course, is not to become too wedded to the draft hypothesis or thesis. Just about every working thesis gets changed during the research process.
CrashCourse Video: "Sociology Research Methods" (YouTube)
Although this video is tailored to sociology students, it is applicable to students in a variety of social science disciplines. This video does a good job demonstrating the connection between the brainstorming that goes into selecting a research question and the formulation of a working hypothesis.
"How to Write a Thesis Statement for an Analytical Essay" (YouTube)
Students writing analytical essays will not develop the same type of working hypothesis as students who are writing research papers in other disciplines. For these students, developing the working thesis may happen as a part of the rough draft (see the relevant section below).
"Research Hypothesis" (Oakland Univ.)
This resource provides some examples of hypotheses in social science disciplines like Political Science and Criminal Justice. These sample hypotheses may also be useful for students in other soft social sciences and humanities disciplines like History.
When grading a research paper, instructors look for a consistent methodology. This section will help you understand different methodological approaches used in research papers. Students will get the most out of these resources if they use them to help prepare for conversations with teachers or discussions in class.
"Types of Research Designs" (USC)
A "research design," used for complex papers, is related to the paper's method. This resource contains introductions to a variety of popular research designs in the social sciences. Although it is not the most intuitive site to read, the information here is very valuable.
"Major Research Methods" (YouTube)
Although this video is a bit on the dry side, it provides a comprehensive overview of the major research methodologies in a format that might be more accessible to students who have struggled with textbooks or other written resources.
"Humanities Research Strategies" (USC)
This is a portal where students can learn about four methodological approaches for humanities papers: Historical Methodologies, Textual Criticism, Conceptual Analysis, and the Synoptic method.
"Selected Major Social Science Research Methods: Overview" (National Academies Press)
This appendix from the book Using Science as Evidence in Public Policy , printed by National Academies Press, introduces some methods used in social science papers.
"Organizing Your Social Sciences Research Paper: 6. The Methodology" (USC)
This resource from the University of Southern California's library contains tips for writing a methodology section in a research paper.
Anyone who is new to writing research papers should be sure to select a method in consultation with their instructor. These resources can be used to help prepare for that discussion. They may also be used on their own by more advanced students.
"Choosing Appropriate Research Methodologies" (Palgrave Study Skills)
This friendly and approachable resource from Palgrave Macmillan can be used by students who are just starting to think about appropriate methodologies.
"How to Choose Your Research Methods" (NFER (UK))
This is another approachable resource students can use to help narrow down the most appropriate methods for their research projects.
The resources in this section introduce the process of gathering scholarly sources and collecting evidence. You'll find a range of material here, from introductory guides to advanced explications best suited to college students. Please consult the LitCharts How to Do Academic Research guide for a more comprehensive list of resources devoted to finding scholarly literature.
Google Scholar
Students who have access to library websites with detailed research guides should start there, but people who do not have access to those resources can begin their search for secondary literature here.
"Gathering Appropriate Information" (Texas Gateway)
This resource from the Texas Gateway for online resources introduces students to the research process, and contains interactive exercises. The level of complexity is suitable for middle school, high school, and introductory college classrooms.
"An Overview of Quantitative and Qualitative Data Collection Methods" (NSF)
This PDF from the National Science Foundation goes into detail about best practices and pitfalls in data collection across multiple types of methodologies.
"Social Science Methods for Data Collection and Analysis" (Swiss FIT)
This resource is appropriate for advanced undergraduates or teachers looking to create lessons on research design and data collection. It covers techniques for gathering data via interviews, observations, and other methods.
"Collecting Data by In-depth Interviewing" (Leeds Univ.)
This resource contains enough information about conducting interviews to make it useful for teachers who want to create a lesson plan, but is also accessible enough for college juniors or seniors to make use of it on their own.
There is no "one size fits all" outlining technique. Some students might devote all their energy and attention to the outline in order to avoid the paper. Other students may benefit from being made to sit down and organize their thoughts into a lengthy sentence outline. The resources in this section include strategies and templates for multiple types of outlines.
"Topic vs. Sentence Outlines" (UC Berkeley)
This resource introduces two basic approaches to outlining: the shorter topic-based approach, and the longer, more detailed sentence-based approach. This resource also contains videos on how to develop paper paragraphs from the sentence-based outline.
"Types of Outlines and Samples" (Purdue OWL)
The Purdue Online Writing Lab's guide is a slightly less detailed discussion of different types of outlines. It contains several sample outlines.
"Writing An Outline" (Austin C.C.)
This resource from a community college contains sample outlines from an American history class that students can use as models.
"How to Structure an Outline for a College Paper" (YouTube)
This brief (sub-2 minute) video from the ExpertVillage YouTube channel provides a model of outline writing for students who are struggling with the idea.
"Outlining" (Harvard)
This is a good resource to consult after completing a draft outline. It offers suggestions for making sure your outline avoids things like unnecessary repetition.
As with outlines, rough drafts can take on many different forms. These resources introduce teachers and students to the various approaches to writing a rough draft. This section also includes resources that will help you cite your sources appropriately according to the MLA, Chicago, and APA style manuals.
"Creating a Rough Draft for a Research Paper" (Univ. of Minnesota)
This resource is useful for teachers in particular, as it provides some suggested exercises to help students with writing a basic rough draft.
Rough Draft Assignment (Duke of Definition)
This sample assignment, with a brief list of tips, was developed by a high school teacher who runs a very successful and well-reviewed page of educational resources.
"Creating the First Draft of Your Research Paper" (Concordia Univ.)
This resource will be helpful for perfectionists or procrastinators, as it opens by discussing the problem of avoiding writing. It also provides a short list of suggestions meant to get students writing.
There is no such thing as a rough draft of a scholarly citation. These links to the three major citation guides will ensure that your citations follow the correct format. Please consult the LitCharts How to Cite Your Sources guide for more resources.
Chicago Manual of Style Citation Guide
Some call The Chicago Manual of Style , which was first published in 1906, "the editors' Bible." The manual is now in its 17th edition, and is popular in the social sciences, historical journals, and some other fields in the humanities.
APA Citation Guide
According to the American Psychological Association, this guide was developed to aid reading comprehension, clarity of communication, and to reduce bias in language in the social and behavioral sciences. Its first full edition was published in 1952, and it is now in its sixth edition.
MLA Citation Guide
The Modern Language Association style is used most commonly within the liberal arts and humanities. The MLA Style Manual and Guide to Scholarly Publishing was first published in 1985 and (as of 2008) is in its third edition.
Any professional scholar will tell you that the best research papers are made in the revision stage. No matter how strong your research question or working thesis, it is not possible to write a truly outstanding paper without devoting energy to revision. These resources provide examples of revision exercises for the classroom, as well as tips for students working independently.
"The Art of Revision" (Univ. of Arizona)
This resource provides a wealth of information and suggestions for both students and teachers. There is a list of suggested exercises that teachers might use in class, along with a revision checklist that is useful for teachers and students alike.
"Script for Workshop on Revision" (Vanderbilt University)
Vanderbilt's guide for leading a 50-minute revision workshop can serve as a model for teachers who wish to guide students through the revision process during classtime.
"Revising Your Paper" (Univ. of Washington)
This detailed handout was designed for students who are beginning the revision process. It discusses different approaches and methods for revision, and also includes a detailed list of things students should look for while they revise.
"Revising Drafts" (UNC Writing Center)
This resource is designed for students and suggests things to look for during the revision process. It provides steps for the process and has a FAQ for students who have questions about why it is important to revise.
No writer is so good that he or she can't benefit from meeting with instructors or peer tutors. These resources from university writing, learning, and communication centers provide suggestions for how to get the most out of these one-on-one meetings.
"Getting Feedback" (UNC Writing Center)
This very helpful resource talks about how to ask for feedback during the entire writing process. It contains possible questions that students might ask when developing an outline, during the revision process, and after the final draft has been graded.
"Prepare for Your Tutoring Session" (Otis College of Art and Design)
This guide from a university's student learning center contains a lot of helpful tips for getting the most out of working with a writing tutor.
"The Importance of Asking Your Professor" (Univ. of Waterloo)
This article from the university's Writing and Communication Centre's blog contains some suggestions for how and when to get help from professors and Teaching Assistants.
Once you've revised your first draft, you're well on your way to handing in a polished paper. These resources—each of them produced by writing professionals at colleges and universities—outline the steps required in order to produce a final draft. You'll find proofreading tips and checklists in text and video form.
"Developing a Final Draft of a Research Paper" (Univ. of Minnesota)
While this resource contains suggestions for revision, it also features a couple of helpful checklists for the last stages of completing a final draft.
Basic Final Draft Tips and Checklist (Univ. of Maryland-University College)
This short and accessible resource, part of UMUC's very thorough online guide to writing and research, contains a very basic checklist for students who are getting ready to turn in their final drafts.
Final Draft Checklist (Everett C.C.)
This is another accessible final draft checklist, appropriate for both high school and college students. It suggests reading your essay aloud at least once.
"How to Proofread Your Final Draft" (YouTube)
This video (approximately 5 minutes), produced by Eastern Washington University, gives students tips on proofreading final drafts.
"Proofreading Tips" (Georgia Southern-Armstrong)
This guide will help students learn how to spot common errors in their papers. It suggests focusing on content and editing for grammar and mechanics.
This final set of resources is intended specifically for high school and college instructors. It provides links to unit plans and classroom exercises that can help improve students' research and writing skills. You'll find resources that give an overview of the process, along with activities that focus on how to begin and how to carry out research.
"Research Paper Complete Resources Pack" (Teachers Pay Teachers)
This packet of assignments, rubrics, and other resources is designed for high school students. The resources in this packet are aligned to Common Core standards.
"Research Paper—Complete Unit" (Teachers Pay Teachers)
This packet of assignments, notes, PowerPoints, and other resources has a 4/4 rating with over 700 ratings. It is designed for high school teachers, but might also be useful to college instructors who work with freshmen.
"Teaching Students to Write Good Papers" (Yale)
This resource from Yale's Center for Teaching and Learning is designed for college instructors, and it includes links to appropriate activities and exercises.
"Research Paper Writing: An Overview" (CUNY Brooklyn)
CUNY Brooklyn offers this complete lesson plan for introducing students to research papers. It includes an accompanying set of PowerPoint slides.
"Lesson Plan: How to Begin Writing a Research Paper" (San Jose State Univ.)
This lesson plan is designed for students in the health sciences, so teachers will have to modify it for their own needs. It includes a breakdown of the brainstorming, topic selection, and research question process.
"Quantitative Techniques for Social Science Research" (Univ. of Pittsburgh)
This is a set of PowerPoint slides that can be used to introduce students to a variety of quantitative methods used in the social sciences.
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Writing a research paper is both an art and a skill, and knowing how to write the methods section of a research paper is the first crucial step in mastering scientific writing. If, like the majority of early career researchers, you believe that the methods section is the simplest to write and needs little in the way of careful consideration or thought, this article will help you understand it is not 1 .
We have all probably asked our supervisors, coworkers, or search engines “ how to write a methods section of a research paper ” at some point in our scientific careers, so you are not alone if that’s how you ended up here. Even for seasoned researchers, selecting what to include in the methods section from a wealth of experimental information can occasionally be a source of distress and perplexity.
Additionally, journal specifications, in some cases, may make it more of a requirement rather than a choice to provide a selective yet descriptive account of the experimental procedure. Hence, knowing these nuances of how to write the methods section of a research paper is critical to its success. The methods section of the research paper is not supposed to be a detailed heavy, dull section that some researchers tend to write; rather, it should be the central component of the study that justifies the validity and reliability of the research.
Are you still unsure of how the methods section of a research paper forms the basis of every investigation? Consider the last article you read but ignore the methods section and concentrate on the other parts of the paper . Now think whether you could repeat the study and be sure of the credibility of the findings despite knowing the literature review and even having the data in front of you. You have the answer!
Having established the importance of the methods section , the next question is how to write the methods section of a research paper that unifies the overall study. The purpose of the methods section , which was earlier called as Materials and Methods , is to describe how the authors went about answering the “research question” at hand. Here, the objective is to tell a coherent story that gives a detailed account of how the study was conducted, the rationale behind specific experimental procedures, the experimental setup, objects (variables) involved, the research protocol employed, tools utilized to measure, calculations and measurements, and the analysis of the collected data 2 .
In this article, we will take a deep dive into this topic and provide a detailed overview of how to write the methods section of a research paper . For the sake of clarity, we have separated the subject into various sections with corresponding subheadings.
Table of Contents
The methods section is a fundamental section of any paper since it typically discusses the ‘ what ’, ‘ how ’, ‘ which ’, and ‘ why ’ of the study, which is necessary to arrive at the final conclusions. In a research article, the introduction, which serves to set the foundation for comprehending the background and results is usually followed by the methods section, which precedes the result and discussion sections. The methods section must explicitly state what was done, how it was done, which equipment, tools and techniques were utilized, how were the measurements/calculations taken, and why specific research protocols, software, and analytical methods were employed.
The primary goal of the methods section is to provide pertinent details about the experimental approach so that the reader may put the results in perspective and, if necessary, replicate the findings 3 . This section offers readers the chance to evaluate the reliability and validity of any study. In short, it also serves as the study’s blueprint, assisting researchers who might be unsure about any other portion in establishing the study’s context and validity. The methods plays a rather crucial role in determining the fate of the article; an incomplete and unreliable methods section can frequently result in early rejections and may lead to numerous rounds of modifications during the publication process. This means that the reviewers also often use methods section to assess the reliability and validity of the research protocol and the data analysis employed to address the research topic. In other words, the purpose of the methods section is to demonstrate the research acumen and subject-matter expertise of the author(s) in their field.
Similar to the research paper, the methods section also follows a defined structure; this may be dictated by the guidelines of a specific journal or can be presented in a chronological or thematic manner based on the study type. When writing the methods section , authors should keep in mind that they are telling a story about how the research was conducted. They should only report relevant information to avoid confusing the reader and include details that would aid in connecting various aspects of the entire research activity together. It is generally advisable to present experiments in the order in which they were conducted. This facilitates the logical flow of the research and allows readers to follow the progression of the study design.
It is also essential to clearly state the rationale behind each experiment and how the findings of earlier experiments informed the design or interpretation of later experiments. This allows the readers to understand the overall purpose of the study design and the significance of each experiment within that context. However, depending on the particular research question and method, it may make sense to present information in a different order; therefore, authors must select the best structure and strategy for their individual studies.
In cases where there is a lot of information, divide the sections into subheadings to cover the pertinent details. If the journal guidelines pose restrictions on the word limit , additional important information can be supplied in the supplementary files. A simple rule of thumb for sectioning the method section is to begin by explaining the methodological approach ( what was done ), describing the data collection methods ( how it was done ), providing the analysis method ( how the data was analyzed ), and explaining the rationale for choosing the methodological strategy. This is described in detail in the upcoming sections.
Contrary to widespread assumption, the methods section of a research paper should be prepared once the study is complete to prevent missing any key parameter. Hence, please make sure that all relevant experiments are done before you start writing a methods section . The next step for authors is to look up any applicable academic style manuals or journal-specific standards to ensure that the methods section is formatted correctly. The methods section of a research paper typically constitutes materials and methods; while writing this section, authors usually arrange the information under each category.
The materials category describes the samples, materials, treatments, and instruments, while experimental design, sample preparation, data collection, and data analysis are a part of the method category. According to the nature of the study, authors should include additional subsections within the methods section, such as ethical considerations like the declaration of Helsinki (for studies involving human subjects), demographic information of the participants, and any other crucial information that can affect the output of the study. Simply put, the methods section has two major components: content and format. Here is an easy checklist for you to consider if you are struggling with how to write the methods section of a research paper .
Now that you know how to write the methods section of a research paper , let’s address another challenge researchers face while writing the methods section —what to include in the methods section . How much information is too much is not always obvious when it comes to trying to include data in the methods section of a paper. In the next section, we examine this issue and explore potential solutions.
The technical nature of the methods section occasionally makes it harder to present the information clearly and concisely while staying within the study context. Many young researchers tend to veer off subject significantly, and they frequently commit the sin of becoming bogged down in itty bitty details, making the text harder to read and impairing its overall flow. However, the best way to write the methods section is to start with crucial components of the experiments. If you have trouble deciding which elements are essential, think about leaving out those that would make it more challenging to comprehend the context or replicate the results. The top-down approach helps to ensure all relevant information is incorporated and vital information is not lost in technicalities. Next, remember to add details that are significant to assess the validity and reliability of the study. Here is a simple checklist for you to follow ( bonus tip: you can also make a checklist for your own study to avoid missing any critical information while writing the methods section ).
To address “ how to write the methods section of a research paper ”, authors should not only pay careful attention to what to include but also what not to include in the methods section of a research paper . Here is a list of do not’s when writing the methods section :
We hope that by this point, you understand how crucial it is to write a thoughtful and precise methods section and the ins and outs of how to write the methods section of a research paper . To restate, the entire purpose of the methods section is to enable others to reproduce the results or verify the research. We sincerely hope that this post has cleared up any confusion and given you a fresh perspective on the methods section .
As a parting gift, we’re leaving you with a handy checklist that will help you understand how to write the methods section of a research paper . Feel free to download this checklist and use or share this with those who you think may benefit from it.
References
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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.
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
Types of research methodology.
There are three types of research methodology based on the type of research and the data required. 1
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.
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:
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
Quantitative research 6
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:
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:
Qualitative research involves an inductive method for data analysis where hypotheses are developed after data collection. The methods include:
Here are some important factors to consider when choosing a research methodology: 8
How to write a research methodology .
A research methodology should include the following components: 3,9
The methods section is a critical part of the research papers, allowing researchers to use this to understand your findings and replicate your work when pursuing their own research. However, it is usually also the most difficult section to write. This is where Paperpal can help you overcome the writer’s block and create the first draft in minutes with Paperpal Copilot, its secure generative AI feature suite.
With Paperpal you can get research advice, write and refine your work, rephrase and verify the writing, and ensure submission readiness, all in one place. Here’s how you can use Paperpal to develop the first draft of your methods section.
You can repeat this process to develop each section of your research manuscript, including the title, abstract and keywords. Ready to write your research papers faster, better, and without the stress? Sign up for Paperpal and start writing today!
Q1. What are the key components of research methodology?
A1. A good research methodology has the following key components:
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
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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Nature Communications volume 15 , Article number: 7806 ( 2024 ) Cite this article
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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.
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 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.
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 ).
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.
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.
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.
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.
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.
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.
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.
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.
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.
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 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.
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.
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.
Designed and adopted by PASTE 27 , Mapping Accuracy calculates the weighted percentage of anchors joining spots with same annotation.
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.
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.
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.
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.
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.
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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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.
Correspondence to Chao Liu , Yuxiang Li , Yong Zhang or Shuangsang Fang .
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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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DOI : https://doi.org/10.1038/s41467-024-51935-0
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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
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
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.
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.
The online version contains supplementary material available at 10.1007/s40272-024-00641-x.
More than half of the patients receiving high-alert medications were affected by a total of 3203 symptoms observed after drug–drug interactions involving high-alert medications. More than one in four observed symptoms were associated with a drug–drug interaction at a significant odds ratio. |
Specific drug pairs were identified that may increase the risk of interaction-associated symptoms, mainly categorized as hemodynamic alterations and fluid and electrolyte balance disturbances. Those drug pairs involved eight drugs frequently administered in a PICU. |
Physicians should avoid the administration of these specific drug pairs, or if their administration is unavoidable, monitor patients closely for corresponding symptoms. |
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.
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.
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.
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.
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
Characteristics | Value |
---|---|
Number of patients, (m/f) | 315 (183/132) |
Median age, years (Q25/Q75; min/max) | 3.7 (0.8/11.3; 0.0/22.8) |
Median weight, kg (Q25/Q75; min/max) | 13.0 (6.7/29.0; 2.3/156.0) |
Median length of PICU stay, days (Q25/Q75; min/max) | 8 (4/14; 3/99) |
Median simplified acute physiology score on PICU admission (Q25/Q75; min/max) | 13 (9/21; 2/50) |
Status of ventilation at PICU admission, (%) | |
Not ventilated | 151 (47.9) |
Non-invasive ventilation | 116 (36.8) |
Invasive ventilation | 48 (15.3) |
Death, (%) | 6 (1.9) |
Primary reason for PICU admission, (%) | |
Surgical | 167 (53.0) |
Gastrointestinal | 53 (16.8) |
Musculoskeletal | 40 (12.7) |
Neurologic | 25 (7.9) |
Oncologic | 23 (7.3) |
Ears-nose-throat/maxillofacial | 13 (4.1) |
Urologic | 7 (2.2) |
Other | 6 (1.9) |
Medical | 141 (44.8) |
Respiratory | 60 (19.0) |
Neurologic | 20 (6.3) |
Sepsis | 15 (4.8) |
Gastrointestinal | 13 (4.1) |
Metabolic | 11 (3.5) |
Cardiovascular | 5 (1.2) |
Other | 17 (5.4) |
Trauma | 7 (2.2) |
PICU pediatric intensive care unit
Characteristics of drug therapy
Characteristics | Value |
---|---|
Total number of administered drugs, | 43,200 |
Number of different administered drugs, | 255 |
Median number of drugs per patient per day, (Q25/Q75; min/max) | 10 (7/15;1/34) |
Total number of administered HAM, / (%) | 5385/43,200 (12.5) |
Number of different administered HAM, / (%) | 15/255 (5.9) |
Median number of HAM per patient per day, (Q25/Q75; min/max) | 1 (0/2; 0/8) |
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 ]
High-alert medication | Number of patients receiving the high-alert medication, (%) ( = 315 patients) | Number of patient days with the high-alert medication, (%) ( = 3788 patient days) |
---|---|---|
Potassium salts | 149 (47.3) | 1477 (39.0) |
Midazolam | 173 (54.9) | 1011 (26.7) |
Vancomycin | 33 (10.5) | 449 (11.9) |
Epinephrine | 74 (23.5) | 431 (11.4) |
Clonidine | 30 (9.5) | 415 (11.0) |
Phenobarbital | 65 (20.6) | 405 (10.7) |
Fentanyl | 42 (13.3) | 389 (10.3) |
Digoxin | 14 (4.4) | 302 (8.0) |
Amphotericin B | 13 (4.1) | 131 (3.5) |
Tacrolimus | 12 (3.8) | 127 (3.4) |
Propofol | 40 (12.7) | 84 (2.2) |
Dobutamine | 7 (2.2) | 69 (1.8) |
Norepinephrine | 18 (5.7) | 35 (0.9) |
Morphine | 12 (3.8) | 33 (0.9) |
Dopamine | 3 (1.0) | 27 (0.7) |
The remaining five high-alert medications were not administered during the study period: cyclosporine, phenytoin, amiodarone, vecuronium, and rocuronium
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.
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
Symptom | Frequency of symptoms, | Frequency related to total of symptoms, % ( = 3203) | Frequency of patients affected by the respective symptom after a pDDI involving HAM, (%) ( = 256 patients receiving HAM) |
---|---|---|---|
Increased heart rate | 781 | 24.4 | 62 (24.2) |
Hyponatremia | 390 | 12.2 | 52 (20.3) |
Vomiting | 262 | 8.2 | 41 (16.0) |
Hypokalemia | 243 | 7.6 | 18 (7.0) |
Decreased blood pressure | 237 | 7.4 | 28 (10.9) |
Respiratory depression | 164 | 5.1 | 24 (9.4) |
Urinary retention | 137 | 4.3 | 29 (11.3) |
Hyperkalemia | 131 | 4.1 | 43 (16.8) |
Edema | 128 | 4.0 | 13 (5.1) |
Nausea | 119 | 3.7 | 24 (9.4) |
Agitation | 118 | 3.7 | 21 (8.2) |
Decreased diuresis | 112 | 3.5 | 23 (9.0) |
Decreased heart rate | 96 | 3.0 | 10 (3.9) |
Hypomagnesemia | 57 | 1.8 | 14 (5.5) |
Sweating | 46 | 1.4 | 9 (3.5) |
Hypocalcemia | 43 | 1.3 | 12 (4.7) |
Increased blood pressure | 43 | 1.3 | 12 (4.7) |
Fever | 19 | 0.6 | 12 (4.7) |
Dyspnea | 14 | 0.4 | 7 (2.7) |
Seizures | 14 | 0.4 | 5 (2.0) |
Constipation | 10 | 0.3 | 4 (1.6) |
Diarrhea | 9 | 0.3 | 2 (0.8) |
Dizziness | 8 | 0.2 | 3 (1.2) |
Abdominal pain | 5 | 0.2 | 3 (1.2) |
Sedation | 4 | 0.1 | 1 (0.4) |
Excessive diuresis | 3 | 0.1 | 2 (0.8) |
Hypercalcemia | 3 | 0.1 | 2 (0.8) |
Increased PTH | 3 | 0.1 | 1 (0.4) |
Exanthema | 2 | 0.1 | 2 (0.8) |
Tachypnea | 2 | 0.1 | 2 (0.8) |
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
pDDI | Classification | Associated symptom | Patient days with/without pDDI and symptom, | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
Drug 1 | Drug 2 | UpToDate | drugs.com | pDDI | Yes | Yes | No | No | Odds ratio [95% CI] | value | |
Symptom | Yes | No | Yes | No | |||||||
Potassium salts | Furosemide | B | n/a | Hyponatremia | 163 | 667 | 341 | 2617 | 1.88 [1.5; 2.3] | < 0.001* | |
Fentanyl | Furosemide | C | Moderate | Decreased blood pressure | 43 | 275 | 104 | 3366 | 5.06 [3.5; 7.4] | < 0.001* | |
Urinary retention | 86 | 232 | 541 | 2929 | 2.01 [1.5; 2.6] | < 0.001* | |||||
Increased heart rate | 76 | 242 | 521 | 2949 | 1.78 [1.3; 2.3] | < 0.001* | |||||
Vancomycin | Furosemide | n/a | Moderate | Edema | 83 | 150 | 490 | 3065 | 3.46 [2.6; 4.6] | < 0.001* | |
Decreased diuresis | 42 | 191 | 575 | 2980 | 1.14 [0.8; 1.6] | 0.459 | |||||
Vomiting | 36 | 197 | 502 | 3053 | 1.11 [0.8; 1.6] | 0.573 | |||||
Digoxin | Furosemide | n/a | Moderate | Hypokalemia | 89 | 134 | 523 | 3042 | 3.86 [2.9; 5.1] | < 0.001* | |
Nausea | 10 | 213 | 177 | 3388 | 0.90 [0.5; 1.7] | 0.748 | |||||
Increased heart rate | 35 | 188 | 562 | 3003 | 0.99 [0.7; 1.4] | 0.978 | |||||
Hypomagnesemia | 12 | 211 | 238 | 3327 | 0.80 [0.4; 1.4] | 0.451 | |||||
Digoxin | HCT | n/a | Moderate | Hypokalemia | 86 | 120 | 526 | 3056 | 4.16 [3.1; 5.6] | < 0.001* | |
Increased heart rate | 29 | 177 | 568 | 3014 | 0.87 [0.6; 1.3] | 0.496 | |||||
Fentanyl | Phenobarbital | D | Major | Restlessness | 80 | 59 | 961 | 2688 | 3.79 [2.7; 5.4] | < 0.001* | |
Sweating | 30 | 109 | 480 | 3169 | 1.82 [1.2; 2.8] | 0.005* | |||||
Potassium salts | HCT | B | n/a | Hyponatremia | 85 | 229 | 419 | 3055 | 2.71 [2.1; 3.5] | < 0.001* | |
Midazolam | HCT | n/a | Moderate | Decreased blood pressure | 20 | 168 | 127 | 3473 | 3.26 [2.0; 5.3] | < 0.001* | |
Increased heart rate | 56 | 132 | 541 | 3059 | 2.40 [1.7; 3.3] | < 0.001* |
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”
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 ].
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 ].
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.
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.
We thank all the physicians and nurses in the participating PICU for their helpful collaboration.
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.
BMC Cancer volume 24 , Article number: 1109 ( 2024 ) Cite this article
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Meaningful engagement with stakeholders in research demands intentional approaches. This paper describes the development of a framework to guide stakeholder engagement as research partners in a pragmatic trial proposed to evaluate behavioral interventions for dysphagia in head and neck cancer patients. We highlight the core principles of stakeholder engagement including representation of all perspectives, meaningful participation, respectful partnership with stakeholders, and accountability to stakeholders; and describe how these principles were operationalized to engage relevant stakeholders throughout the course of a large clinical trial.
Peer Review reports
Approaches to patient involvement and stakeholder engagement have blossomed in the past decade as important ways to improve quality of health care and research [ 1 , 2 , 3 , 4 , 5 , 6 ]. While patient refers to individuals living with an impaired health condition, stakeholder includes any individual who could be affected by or have a connection with a topic area as a clinician, knowledge user, caregiver, or decision-maker [ 7 ]. Stakeholder engagement has roots in the Community Based Participatory Research (CBPR) movement of the 1980s and 1990s, in which all individuals involved in the outcome of a community-based intervention have a say [ 8 , 9 , 10 ]. Early efforts towards expanding stakeholder engagement into clinical care focused primarily on quality improvement efforts where clinicians involved patients as partners in their own plan of care and engaged them as self-care or self-management agents [ 11 , 12 ]. These efforts expanded to including patients as partners in co-designing new programs and clinical care models aimed at improving patient experience and the quality of care [ 13 ]. Stakeholder engagement is now a key strategy in designing, investigating, and implementing person-centered philosophies or approaches in health care.
More recently, researchers have incorporated a range of stakeholders, including patients, in their initiatives [ 14 , 15 , 16 ]. For research endeavors, stakeholder engagement aims to increase the likelihood of investigating research questions important to patients and utilizing patient-centered research methods. Researchers have also included stakeholders to accomplish several objectives during the research project implementation: (1) effective participant recruitment, (2) collection of data relevant to stakeholders, and (3) and interpretation of results from multiple stakeholder perspectives [ 14 ]. Additionally, stakeholder engagement has also been used to ensure successful dissemination, uptake, and implementation of research findings. Ultimately, the impact of research could be enhanced by involving relevant stakeholders throughout the life cycles of the research effort (e.g., planning, study conduct, dissemination/implementation) rather than participating in only a limited part of the process (e.g., endorsement of a trial concept).
Literature describing how researchers engage stakeholders has grown steadily [ 17 , 18 ]. Specifically, levels of engagement range from consulting on priorities for investigations before research begins, to serving on steering or advisory committees, to full membership on a research team [ 19 , 20 ]. Likewise, the timing of engagement can vary. It may occur early, prior to proposal design with a workshop to identify research questions or, during the proposal development and writing. It may also occur only once funding has been awarded and implementation commences or after study completion to discuss findings and plans for action. Various challenges have been identified as researchers seek to engage stakeholders resulting in recommendations to avoid tokenism, to budget sufficient time and energy for each step in the engagement process, and to provide specific training and resources to stakeholders to support engagement [ 3 , 4 ]. One significant observation is the challenge for all involved to hold non-judgmental, open attitudes about engagement and find ways to interact respectfully and collaboratively. Another is the challenge of finding strategies to overcome the inherent power imbalance that can exist in groups where patients and healthcare clinicians are together [ 21 ].
For engagement work with clinical populations with distinct needs, based on our assessment of the existing literature, it is unclear whether general principles of stakeholder engagement are useful across all research initiatives or if special approaches are required for particular disease groups, such as certain types of cancer. Protocols demonstrating how to engage stakeholders successfully, what constitutes success for various levels of engagement, and systematic evaluation of engagement models remains sparsely reported in cancer trials overall [ 4 , 22 ]. Within cancer research, head and neck cancers (HNC) represent a unique population who experience functional changes that distinguish their experience from other survivors (e.g., changes in fundamental activities like eating, talking, and their appearance) – this distinction is empirically relevant to the composition and operationalization of stakeholder engagement for this cancer survivor population. Yet, there is only one published experience to guide investigators interested in conducting stakeholder engaged research in the HNC populations [ 23 ].
No uniform model exists for stakeholder engagement overall or in specific clinical populations, and few presented in the literature have evolved from empirical work [ 6 ]. There is growing agreement that it is important to involve stakeholders as early as possible in the research process and engage them in on-going evaluation and feedback about the process of engagement. Therefore, an urgent need exists for protocols that detail population specific models for stakeholder engaged clinical research.
The aim of this work is to describe the protocol for engaging stakeholders in the implementation of a pragmatic trial in a specific HNC clinical cancer population. [ 24 ] We will operationalize the model structure, the principles that will guide the model and the overall engagement plan across the various stages of the trial.
As the Engagement Team for an international, multi-site pragmatic trial aimed at comparing the effectiveness of swallowing interventions during radiotherapy (RT) for HNC (PRO-ACTIVE), we adopted four core principles of stakeholder engagement based on our interpretation of national guidelines for stakeholder engagement, like those from the Canadian Institutes of Health Research (CIHR) [ 7 ], National Institute for Health and Care Research (NIHR), [ 25 ] and in the existing literature [ 1 , 4 , 5 , 6 , 7 , 8 , 26 , 27 , 28 , 29 ]. We paid special attention to the stakeholder engagement guidelines provided by our funder, the Patient-Centered Outcomes Research Institute (PCORI) [ 26 , 27 ]. In particular, we focused on principles emphasizing that all involved in the outcomes should be represented, that mutuality in stakeholder relationships be established, that ongoing relations between the trial team and various stakeholders be reciprocal, and that our plan be guided by opportunities for mutual learning between stakeholders and trial leadership. We also needed to meet the complex and varied needs of a pragmatic, multi-site, randomized clinical trial for a behavioral intervention to be conducted in both the USA and Canada [ 24 ]. We adapted these principles for our purposes and intended outcomes in our plan for engaging with stakeholders throughout the duration of the trial in a HNC population.
Dysphagia is a major problem facing individuals undergoing RT for HNC. Questions remain about the most effective timing and intensity of swallowing interventions during RT. PRO-ACTIVE is an international multi-center 3-arm pragmatic randomized controlled trial (RCT) which aims to compare the effectiveness of PRO-ACTIVE versus RE-ACTIVE behavioral swallowing interventions among 952 patients with HNC scheduled to undergo radiotherapy [ 24 ]. Before commencing RT, eligible patients will be randomized to receive one of three therapy models delivered by oncology-specialized speech language pathologists (SLPs) during the 6–7 weeks of radiotherapy. Regardless of the Arm, all patients will receive a swallow assessment 3 and 12 months after RT completion. The 3 intervention arms are:
Arm 1 (RE-ACTIVE): Reactive intervention started promptly if/when dysphagia is identified;
Arm 2 (PRO-ACTIVE EAT): Early low intensity proactive intervention started before RT commences;
Arm 3 (PRO-ACTIVE EAT + EXERCISE): Early high intensity proactive intervention started before RT commences.
Dysphagia is common following RT, and swallowing function is of high priority for patients, caregivers, and therapy providers [ 30 , 31 , 32 ]. There are, however, differences in perspective on the manner in which dysphagia impacts health. Patients prioritize issues of depression/anxiety whereas caregivers and clinicians prioritize pneumonia/malnutrition [ 30 , 33 ]. The perspective of the payer and policy maker has not before been explored, but given that tube dependency affects costs, we expect payers and policy makers to endorse reduction in tube feeding utilization as a priority issue for quality improvement in the area of dysphagia [ 34 ]. Clearly, patients, caregivers, providers and (we suspect) policy makers have varying perspectives on the most relevant issues for HNC survivors with dysphagia.
This trial is focused on a behavioral intervention which is applied during a notoriously arduous course of treatment and a time when many patients are known to experience a number of toxicities including increasing difficulty swallowing and maintaining their nutritional status [ 35 ]. Numerous physicians and allied healthcare providers work closely with patients and their caregivers during this treatment period. Our approach to stakeholder engagement needed to ensure that these diverse perspectives would be optimally included, and that the complex issues relevant to supportive care in HNC in two distinct healthcare policy environments (Canada and the US) be considered. We also want to ensure policy/payer perspectives are included as future implementation of a successful intervention may include cost implications and require policy change.
Engagement principles.
We designed the PRO-ACTIVE stakeholder engagement model structure and plan for engagement around four key principles derived from the literature and from our collective understanding of the needs of the HNC patient population (see Table 1 ).
Our engagement protocol was designed to ensure that the key principles of representation, meaningful participation, respectful partnership, and accountability are integrated into all our exchanges with stakeholders. How we operationalized these principles or organized the structure and environments to integrate them in our approach is described below.
US and Canadian engagement sites : Because of the important differences in healthcare delivery, reimbursement approaches, and patient experiences between the US and Canada, we designed a comprehensive stakeholder engagement processes to occur at one site in each country. The two sites will work in tandem, with complementary stakeholder panels, under the leadership of the Engagement Team (CM, MF, KH, RM, TM).
Core and Periphery sites : In the US, where a significant proportion of cancer treatment occurs at non-academic settings, stakeholders representing stakeholder and satellite care facilities will be invited. The proximity to a major medical center is also a crucial factor in the feasibility and possible future uptake of therapies. Therefore, representatives from remote, rural areas will be included in each stakeholder panel.
Identifying relevant stakeholders for this investigation requires thinking about both the conduct of the trial itself and the future uptake and utilization of the trial results in an interdisciplinary complex clinical environment. In our situation, relevant stakeholders will include patients/caregivers, physician providers, allied health providers, and policymaker/payers. (See Fig. 1 for target distributions for stakeholder samples.)
Target distributions of characteristics for each USA and Canadian stakeholder research partner panel
In order to ensure meaningful participation among all stakeholders, we will structure the engagement process to equalize differences in understanding of research processes, and to ensure that all stakeholders have a voice in each phase of engagement.
Training : In recognition that stakeholders have different backgrounds related to research, the Engagement Team will provide training regarding research methods as part of the early engagement process. Facilitators will have extensive experience in working with low-literacy stakeholders as well as with professional stakeholders and will be able to provide this training as needed at the initial focus group sessions.
A modular structure based on closed-loop communication : our engagement protocol is based on a series of five task-based modules, with all stakeholders brainstorming solutions to research-related problems, the SAB prioritizing those suggestions and recommending an approach to the research problem, and finally the Trial Executive Committee deciding on stakeholder recommendations and reporting back to stakeholders detailing the research decisions before the start of the next module. (See Fig. 2 )
Proposed Workflow for each six-month long stakeholder engagement module
Homogeneous Brainstorming Panels : Forming homogeneous stakeholder focus groups will empower the various participants to voice their unique perspectives in a safe and comfortable environment by ensuring that participants meet in sessions comprised of those with similar experiences of HNC. We will utilize the homogeneous groups for brainstorming sessions around research-related questions, allowing participants to contribute more freely and to build on common experiences more easily than is possible in mixed groups. Each site will convene four separate stakeholder panels (i.e., patients and family members, MDs, allied clinicians, and policymaker/payer groups). Each patient/caregiver, MD and AH group will meet once in each module to generate ideas around a common topic.
Heterogeneous Stakeholder Advisory Board : Following each brainstorming session, we will convene a stakeholder advisory board composed of 2–3 individuals from each of the homogeneous groups to review the recommendations from each of the brainstorming sessions and discuss their merits. They will develop consensus recommendations based on the discussions of the homogeneous panels which will ultimately be presented to the Trial Executive Committee.
Stakeholder Engagement throughout all phases of research process : Stakeholder consultation and collaboration are intentionally built into all phases of this study, from conception through dissemination. The last module will also include planning the future implementation of the successful dysphagia intervention, including stakeholder involvement. Stakeholder panels in both the USA and Canada will work in parallel with close collaboration by the facilitators who will share agendas, inquiry approaches (questions for the stakeholders to discuss), and experiences with their groups throughout the work. Five modules (See Table 2 ) will guide the dialogue with stakeholders and mirror the phases of the research study (see Fig. 3 for Workflow for Stakeholder Engagement Modules).
Fair compensation : Based on the guidelines outlined by PCORI, stakeholders will be offered stipends commensurate with their usual rates of pay in addition to compensation for travel and accommodations, and meals at engagement sites [ 27 ].
Meetings at neutral sites : To ensure a free exchange of ideas, stakeholder meetings will be held at non-medical, neutral sites, and scheduled to maximize participation. Members from more distant, rural areas will be given the option of participating via remote video conference if they are unable to join in person.
Independent , Professional facilitation : All stakeholder meetings will be facilitated by professional research methodologists, who specialize in focus group and stakeholder engagement facilitation. They will be skilled in ensuring that all voices are heard and accounted for. To ensure free and frank discussion, facilitators should be independent of the trial leadership. All meetings will be audiotaped, de-identified, transcribed and summarized by the facilitators following each session [ 36 ].
Member validation : De-identified notes from each stakeholder panel will be shared among all stakeholders for their validation and further input. Feedback will also be shared from the SAB and the Trial Executive decisions back to the stakeholder panels to ensure closed-loop communication [ 37 ].
Investigator feedback : Once the SABs reach consensus, the Engagement Team will collate the recommendations, prepare a report on that process and recommendations for presentation to the Trial Executive Committee. The Executive Committee members will utilize the report to determine how to incorporate stakeholder recommendations into the trial. Once the final decision has been made, the facilitators will disseminate an accountability report to all stakeholders.
Model of information flow between stakeholders, facilitators, and trial leadership
This paper details a protocol for stakeholder engagement developed specifically for a pragmatic trial of a supportive care intervention during RT for HNC. A model structure and engagement plan were developed for the investigators’ trial design and an anticipated clinical environment for future implementation. Involving stakeholders in clinical trials and clarity about how stakeholder engagement principles will be operationalized in trials is increasingly necessary as funding agencies and investigators recognize the merit of this process by requiring stakeholder engagement in proposal submissions. This tailored protocol offers the first available template for other researchers designing clinical trials with an oncological patient population.
Stakeholder engagement in research is still an emerging phenomenon, especially in terms of how to operationalize engagement principles [ 3 , 38 ]. Questions remain regarding how best to organize and incorporate principles of fair and fruitful engagement of stakeholders in cases of specific trial designs in the context of specific clinical populations [ 11 ]. In planning for a pragmatic trial of a behavioral intervention, we posited that engagement of stakeholders would benefit the conduct of the trial as well as future dissemination and uptake. We anticipated the future uptake will require not only the evidence from the trial regarding the effectiveness of the intervention but also consideration of what is important to patients, how patients and caregivers are able to manage the intervention, and who would support utilization of the trial findings as clinicians or as payers. Therefore, it was important that we include clinicians in all potential sites of care across U.S. and Canada, as well as representatives of payer organizations in both national policy domains.
Meaningful stakeholder input needs to be defined in relation to the type of trial, the practice environment, and both the clinician and patient characteristics. Protocols are needed to guide the uptake and utilization of research results that outline the range of considerations for successful implementation [ 6 , 11 ]. A primary consideration for stakeholder engagement is the careful identification of who would be considered relevant as a stakeholder given the context of the work. Criticism has been made about using generic panels of individuals who are not experienced in the selected clinical setting or care intervention. Engaging individuals who have experience undergoing the treatment processes under consideration can offer unique insights regarding how interventions are most apt to be successful [ 39 , 40 , 41 ]. Behavioral interventions will require understanding both patient and clinician behaviors and motivations as well as the environment where the intervention will be taught and performed.
The clinical care environment for this HNC patient group is complex, with a range of clinicians who hold diverse, and often divergent, perspectives about supportive care and treatment priorities. Also, the behavioral intervention would need to be enacted by patients who are already burdened with increasing pain and difficulty swallowing. They may also be limited by socio-economic burdens and uncertainty. Their capacity to engage with the successful behavioral intervention itself is of paramount importance in the future. It is therefore essential that patients who have undergone RT for HNC and their family members advise on all aspects of the trial. Payers also must be consulted because supportive care interventions may not be seen as central to curative care and they may require additional costs and policy changes to implement the successful intervention in current health services models. This stakeholder engagement protocol, therefore, represents a very specific context of supportive care in cancer but may have relevance to clinical populations with similar interdisciplinary considerations and competing priorities at play.
Central to the our protocol was the composition and manner of engaging stakeholders to fit the context of the trial and follow best practices for engagement. By engaging the relevant stakeholders in the research itself and providing an environment where opinions can be shared respectfully without feelings of intimidation, and meaningful relevant input can be obtained to set the stage for success in the future implementation of study findings. Creating such environments requires intentional organization. Drawing from research in other arenas (e.g., psychology, social work, nursing) offers ideas about working with stakeholders effectively [ 42 , 43 , 44 ]. For example, grouping of individuals with similar experiences (i.e., homogeneous groups) and paying close attention to power dynamics (e.g., who is perceived to be influencers) are helpful. Especially in the world of health care, power dynamics can influence feelings of safety and open dialogue when patients, physicians and/or other health care professionals are grouped together. Separating the stakeholders into homogeneous groups for open brainstorming of ideas, but then organizing a way to build consensus through skilled facilitation, is one approach to overcome this challenge. However, we have made a commitment to report divergent perspectives if consensus cannot be reached.
It is also important to have an independent, trained facilitator to manage the group sessions, who understands the trial environment but does not express a preference for a particular trial outcome [ 45 ]. An added concern for the HNC patient population is the reality that some will have challenges speaking (e.g., because of tongue injury or dry mouth) while others may find it difficult to describe their experiences easily, or in a public situation [ 36 ]. Some may require added time to express their views clearly which could contribute to frustration within a group situation.
Finally, accountability to the stakeholders is not only respectful but also promotes added engagement. That accountability needs to be about how their input was utilized as well as asking them routinely about how the processes for engagement are working. Designing expectations for stakeholder roles and rules of engagement at the onset is an important step to establish the approaches for accountability. Taking the time for all to understand how the engagement principles are to be handled can truly enrich the research process, its outcomes, and the impact of the work.
Stakeholder engagement requires specific strategies to ensure relevant and meaningful engagement for all concerned. We designed a protocol of stakeholder engagement operationalizing basic principles and incorporating it as an integral component within the design of an ongoing clinical trial. Given that understanding how to operationalize these principles within specific contexts is still emerging, our protocol could be helpful to other research teams who wish to incorporate stakeholder engagement in meaningful, relevant ways in a pragmatic clinical trial particularly in the context of interdisciplinary care of head and neck cancers.
No datasets were generated or analysed during the current study.
Community Based Participatory Research
Head and Neck Cancer
Radiotherapy
Canadian Institutes of Health Research
National Institute for Health and Care Research
Patient-Centered Outcomes Research Institute
Randomized Controlled Trial
Speech Language Pathologist
Stakeholder Advisory Board
Doctor of Medicine
Allied Health
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The authors acknowledge Maisha Khan, Melissa Giamou, Veronica Rodriguez, Kieshan Amarakaran and Courtney Field for invaluable feedback to planning final protocol and/or preparing the manuscript.
Patient-Centered Outcomes Research Institute (PCORI), a United States-based non-governmental institute, has funded this project (ID: PCS-1609-36195). PCORI has no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.
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Cameron Macdonald
Bloomberg Faculty of Nursing, University of Toronto, Toronto, ON, Canada
Margaret Fitch
Department of Head and Neck Surgery, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States of America
Katherine A. Hutcheson
Otolaryngology Head and Neck Surgery, Department of Surgery, University of Wisconsin – Madison School of Medicine and Public Health, Madison, WI, United States of America
Timothy M. McCulloch
Department of Speech-Language Pathology, University of Toronto, Toronto, ON, Canada
Rosemary Martino
Krembil Research Institute, University Health Network, Toronto, ON, Canada
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Correspondence to Katherine A. Hutcheson or Rosemary Martino .
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All versions of the study protocol have been reviewed and approved by the Research Ethics Board (REB/Institutional Review Board (IRB) of each site before the trial was implemented. The Princess Margaret Cancer Centre (PM), part of the University Health Network (UHN) in Toronto, ON is the prime site and was reviewed by Clinical Trials Ontario, as the review of record (CTO Project ID: 1363).
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Macdonald, C., Fitch, M., Hutcheson, K.A. et al. A protocol for stakeholder engagement in head and neck cancer pragmatic trials. BMC Cancer 24 , 1109 (2024). https://doi.org/10.1186/s12885-024-12733-5
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Home » Research Methods – Types, Examples and Guide
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Definition:
Research Methods refer to the techniques, procedures, and processes used by researchers to collect , analyze, and interpret data in order to answer research questions or test hypotheses. The methods used in research can vary depending on the research questions, the type of data that is being collected, and the research design.
Types of Research Methods are as follows:
Qualitative research methods are used to collect and analyze non-numerical data. This type of research is useful when the objective is to explore the meaning of phenomena, understand the experiences of individuals, or gain insights into complex social processes. Qualitative research methods include interviews, focus groups, ethnography, and content analysis.
Quantitative research methods are used to collect and analyze numerical data. This type of research is useful when the objective is to test a hypothesis, determine cause-and-effect relationships, and measure the prevalence of certain phenomena. Quantitative research methods include surveys, experiments, and secondary data analysis.
Mixed Method Research refers to the combination of both qualitative and quantitative research methods in a single study. This approach aims to overcome the limitations of each individual method and to provide a more comprehensive understanding of the research topic. This approach allows researchers to gather both quantitative data, which is often used to test hypotheses and make generalizations about a population, and qualitative data, which provides a more in-depth understanding of the experiences and perspectives of individuals.
The following Table shows the key differences between Quantitative, Qualitative and Mixed Research Methods
Research Method | Quantitative | Qualitative | Mixed Methods |
---|---|---|---|
To measure and quantify variables | To understand the meaning and complexity of phenomena | To integrate both quantitative and qualitative approaches | |
Typically focused on testing hypotheses and determining cause and effect relationships | Typically exploratory and focused on understanding the subjective experiences and perspectives of participants | Can be either, depending on the research design | |
Usually involves standardized measures or surveys administered to large samples | Often involves in-depth interviews, observations, or analysis of texts or other forms of data | Usually involves a combination of quantitative and qualitative methods | |
Typically involves statistical analysis to identify patterns and relationships in the data | Typically involves thematic analysis or other qualitative methods to identify themes and patterns in the data | Usually involves both quantitative and qualitative analysis | |
Can provide precise, objective data that can be generalized to a larger population | Can provide rich, detailed data that can help understand complex phenomena in depth | Can combine the strengths of both quantitative and qualitative approaches | |
May not capture the full complexity of phenomena, and may be limited by the quality of the measures used | May be subjective and may not be generalizable to larger populations | Can be time-consuming and resource-intensive, and may require specialized skills | |
Typically focused on testing hypotheses and determining cause-and-effect relationships | Surveys, experiments, correlational studies | Interviews, focus groups, ethnography | Sequential explanatory design, convergent parallel design, explanatory sequential design |
Examples of Research Methods are as follows:
Qualitative Research Example:
A researcher wants to study the experience of cancer patients during their treatment. They conduct in-depth interviews with patients to gather data on their emotional state, coping mechanisms, and support systems.
Quantitative Research Example:
A company wants to determine the effectiveness of a new advertisement campaign. They survey a large group of people, asking them to rate their awareness of the product and their likelihood of purchasing it.
Mixed Research Example:
A university wants to evaluate the effectiveness of a new teaching method in improving student performance. They collect both quantitative data (such as test scores) and qualitative data (such as feedback from students and teachers) to get a complete picture of the impact of the new method.
Research methods are used in various fields to investigate, analyze, and answer research questions. Here are some examples of how research methods are applied in different fields:
Research methods serve several purposes, including:
Research methods are used when you need to gather information or data to answer a question or to gain insights into a particular phenomenon.
Here are some situations when research methods may be appropriate:
Research methods provide several advantages, including:
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Academic journals, archives, and repositories are seeing an increasing number of questionable research papers clearly produced using generative AI. They are often created with widely available, general-purpose AI applications, most likely ChatGPT, and mimic scientific writing. Google Scholar easily locates and lists these questionable papers alongside reputable, quality-controlled research. Our analysis of a selection of questionable GPT-fabricated scientific papers found in Google Scholar shows that many are about applied, often controversial topics susceptible to disinformation: the environment, health, and computing. The resulting enhanced potential for malicious manipulation of society’s evidence base, particularly in politically divisive domains, is a growing concern.
Swedish School of Library and Information Science, University of Borås, Sweden
Department of Arts and Cultural Sciences, Lund University, Sweden
Division of Environmental Communication, Swedish University of Agricultural Sciences, Sweden
The use of ChatGPT to generate text for academic papers has raised concerns about research integrity. Discussion of this phenomenon is ongoing in editorials, commentaries, opinion pieces, and on social media (Bom, 2023; Stokel-Walker, 2024; Thorp, 2023). There are now several lists of papers suspected of GPT misuse, and new papers are constantly being added. 1 See for example Academ-AI, https://www.academ-ai.info/ , and Retraction Watch, https://retractionwatch.com/papers-and-peer-reviews-with-evidence-of-chatgpt-writing/ . While many legitimate uses of GPT for research and academic writing exist (Huang & Tan, 2023; Kitamura, 2023; Lund et al., 2023), its undeclared use—beyond proofreading—has potentially far-reaching implications for both science and society, but especially for their relationship. It, therefore, seems important to extend the discussion to one of the most accessible and well-known intermediaries between science, but also certain types of misinformation, and the public, namely Google Scholar, also in response to the legitimate concerns that the discussion of generative AI and misinformation needs to be more nuanced and empirically substantiated (Simon et al., 2023).
Google Scholar, https://scholar.google.com , is an easy-to-use academic search engine. It is available for free, and its index is extensive (Gusenbauer & Haddaway, 2020). It is also often touted as a credible source for academic literature and even recommended in library guides, by media and information literacy initiatives, and fact checkers (Tripodi et al., 2023). However, Google Scholar lacks the transparency and adherence to standards that usually characterize citation databases. Instead, Google Scholar uses automated crawlers, like Google’s web search engine (Martín-Martín et al., 2021), and the inclusion criteria are based on primarily technical standards, allowing any individual author—with or without scientific affiliation—to upload papers to be indexed (Google Scholar Help, n.d.). It has been shown that Google Scholar is susceptible to manipulation through citation exploits (Antkare, 2020) and by providing access to fake scientific papers (Dadkhah et al., 2017). A large part of Google Scholar’s index consists of publications from established scientific journals or other forms of quality-controlled, scholarly literature. However, the index also contains a large amount of gray literature, including student papers, working papers, reports, preprint servers, and academic networking sites, as well as material from so-called “questionable” academic journals, including paper mills. The search interface does not offer the possibility to filter the results meaningfully by material type, publication status, or form of quality control, such as limiting the search to peer-reviewed material.
To understand the occurrence of ChatGPT (co-)authored work in Google Scholar’s index, we scraped it for publications, including one of two common ChatGPT responses (see Appendix A) that we encountered on social media and in media reports (DeGeurin, 2024). The results of our descriptive statistical analyses showed that around 62% did not declare the use of GPTs. Most of these GPT-fabricated papers were found in non-indexed journals and working papers, but some cases included research published in mainstream scientific journals and conference proceedings. 2 Indexed journals mean scholarly journals indexed by abstract and citation databases such as Scopus and Web of Science, where the indexation implies journals with high scientific quality. Non-indexed journals are journals that fall outside of this indexation. More than half (57%) of these GPT-fabricated papers concerned policy-relevant subject areas susceptible to influence operations. To avoid increasing the visibility of these publications, we abstained from referencing them in this research note. However, we have made the data available in the Harvard Dataverse repository.
The publications were related to three issue areas—health (14.5%), environment (19.5%) and computing (23%)—with key terms such “healthcare,” “COVID-19,” or “infection”for health-related papers, and “analysis,” “sustainable,” and “global” for environment-related papers. In several cases, the papers had titles that strung together general keywords and buzzwords, thus alluding to very broad and current research. These terms included “biology,” “telehealth,” “climate policy,” “diversity,” and “disrupting,” to name just a few. While the study’s scope and design did not include a detailed analysis of which parts of the articles included fabricated text, our dataset did contain the surrounding sentences for each occurrence of the suspicious phrases that formed the basis for our search and subsequent selection. Based on that, we can say that the phrases occurred in most sections typically found in scientific publications, including the literature review, methods, conceptual and theoretical frameworks, background, motivation or societal relevance, and even discussion. This was confirmed during the joint coding, where we read and discussed all articles. It became clear that not just the text related to the telltale phrases was created by GPT, but that almost all articles in our sample of questionable articles likely contained traces of GPT-fabricated text everywhere.
Evidence hacking and backfiring effects
Generative pre-trained transformers (GPTs) can be used to produce texts that mimic scientific writing. These texts, when made available online—as we demonstrate—leak into the databases of academic search engines and other parts of the research infrastructure for scholarly communication. This development exacerbates problems that were already present with less sophisticated text generators (Antkare, 2020; Cabanac & Labbé, 2021). Yet, the public release of ChatGPT in 2022, together with the way Google Scholar works, has increased the likelihood of lay people (e.g., media, politicians, patients, students) coming across questionable (or even entirely GPT-fabricated) papers and other problematic research findings. Previous research has emphasized that the ability to determine the value and status of scientific publications for lay people is at stake when misleading articles are passed off as reputable (Haider & Åström, 2017) and that systematic literature reviews risk being compromised (Dadkhah et al., 2017). It has also been highlighted that Google Scholar, in particular, can be and has been exploited for manipulating the evidence base for politically charged issues and to fuel conspiracy narratives (Tripodi et al., 2023). Both concerns are likely to be magnified in the future, increasing the risk of what we suggest calling evidence hacking —the strategic and coordinated malicious manipulation of society’s evidence base.
The authority of quality-controlled research as evidence to support legislation, policy, politics, and other forms of decision-making is undermined by the presence of undeclared GPT-fabricated content in publications professing to be scientific. Due to the large number of archives, repositories, mirror sites, and shadow libraries to which they spread, there is a clear risk that GPT-fabricated, questionable papers will reach audiences even after a possible retraction. There are considerable technical difficulties involved in identifying and tracing computer-fabricated papers (Cabanac & Labbé, 2021; Dadkhah et al., 2023; Jones, 2024), not to mention preventing and curbing their spread and uptake.
However, as the rise of the so-called anti-vaxx movement during the COVID-19 pandemic and the ongoing obstruction and denial of climate change show, retracting erroneous publications often fuels conspiracies and increases the following of these movements rather than stopping them. To illustrate this mechanism, climate deniers frequently question established scientific consensus by pointing to other, supposedly scientific, studies that support their claims. Usually, these are poorly executed, not peer-reviewed, based on obsolete data, or even fraudulent (Dunlap & Brulle, 2020). A similar strategy is successful in the alternative epistemic world of the global anti-vaccination movement (Carrion, 2018) and the persistence of flawed and questionable publications in the scientific record already poses significant problems for health research, policy, and lawmakers, and thus for society as a whole (Littell et al., 2024). Considering that a person’s support for “doing your own research” is associated with increased mistrust in scientific institutions (Chinn & Hasell, 2023), it will be of utmost importance to anticipate and consider such backfiring effects already when designing a technical solution, when suggesting industry or legal regulation, and in the planning of educational measures.
Recommendations
Solutions should be based on simultaneous considerations of technical, educational, and regulatory approaches, as well as incentives, including social ones, across the entire research infrastructure. Paying attention to how these approaches and incentives relate to each other can help identify points and mechanisms for disruption. Recognizing fraudulent academic papers must happen alongside understanding how they reach their audiences and what reasons there might be for some of these papers successfully “sticking around.” A possible way to mitigate some of the risks associated with GPT-fabricated scholarly texts finding their way into academic search engine results would be to provide filtering options for facets such as indexed journals, gray literature, peer-review, and similar on the interface of publicly available academic search engines. Furthermore, evaluation tools for indexed journals 3 Such as LiU Journal CheckUp, https://ep.liu.se/JournalCheckup/default.aspx?lang=eng . could be integrated into the graphical user interfaces and the crawlers of these academic search engines. To enable accountability, it is important that the index (database) of such a search engine is populated according to criteria that are transparent, open to scrutiny, and appropriate to the workings of science and other forms of academic research. Moreover, considering that Google Scholar has no real competitor, there is a strong case for establishing a freely accessible, non-specialized academic search engine that is not run for commercial reasons but for reasons of public interest. Such measures, together with educational initiatives aimed particularly at policymakers, science communicators, journalists, and other media workers, will be crucial to reducing the possibilities for and effects of malicious manipulation or evidence hacking. It is important not to present this as a technical problem that exists only because of AI text generators but to relate it to the wider concerns in which it is embedded. These range from a largely dysfunctional scholarly publishing system (Haider & Åström, 2017) and academia’s “publish or perish” paradigm to Google’s near-monopoly and ideological battles over the control of information and ultimately knowledge. Any intervention is likely to have systemic effects; these effects need to be considered and assessed in advance and, ideally, followed up on.
Our study focused on a selection of papers that were easily recognizable as fraudulent. We used this relatively small sample as a magnifying glass to examine, delineate, and understand a problem that goes beyond the scope of the sample itself, which however points towards larger concerns that require further investigation. The work of ongoing whistleblowing initiatives 4 Such as Academ-AI, https://www.academ-ai.info/ , and Retraction Watch, https://retractionwatch.com/papers-and-peer-reviews-with-evidence-of-chatgpt-writing/ . , recent media reports of journal closures (Subbaraman, 2024), or GPT-related changes in word use and writing style (Cabanac et al., 2021; Stokel-Walker, 2024) suggest that we only see the tip of the iceberg. There are already more sophisticated cases (Dadkhah et al., 2023) as well as cases involving fabricated images (Gu et al., 2022). Our analysis shows that questionable and potentially manipulative GPT-fabricated papers permeate the research infrastructure and are likely to become a widespread phenomenon. Our findings underline that the risk of fake scientific papers being used to maliciously manipulate evidence (see Dadkhah et al., 2017) must be taken seriously. Manipulation may involve undeclared automatic summaries of texts, inclusion in literature reviews, explicit scientific claims, or the concealment of errors in studies so that they are difficult to detect in peer review. However, the mere possibility of these things happening is a significant risk in its own right that can be strategically exploited and will have ramifications for trust in and perception of science. Society’s methods of evaluating sources and the foundations of media and information literacy are under threat and public trust in science is at risk of further erosion, with far-reaching consequences for society in dealing with information disorders. To address this multifaceted problem, we first need to understand why it exists and proliferates.
Finding 1: 139 GPT-fabricated, questionable papers were found and listed as regular results on the Google Scholar results page. Non-indexed journals dominate.
Most questionable papers we found were in non-indexed journals or were working papers, but we did also find some in established journals, publications, conferences, and repositories. We found a total of 139 papers with a suspected deceptive use of ChatGPT or similar LLM applications (see Table 1). Out of these, 19 were in indexed journals, 89 were in non-indexed journals, 19 were student papers found in university databases, and 12 were working papers (mostly in preprint databases). Table 1 divides these papers into categories. Health and environment papers made up around 34% (47) of the sample. Of these, 66% were present in non-indexed journals.
Indexed journals* | 5 | 3 | 4 | 7 | 19 |
Non-indexed journals | 18 | 18 | 13 | 40 | 89 |
Student papers | 4 | 3 | 1 | 11 | 19 |
Working papers | 5 | 3 | 2 | 2 | 12 |
Total | 32 | 27 | 20 | 60 | 139 |
Finding 2: GPT-fabricated, questionable papers are disseminated online, permeating the research infrastructure for scholarly communication, often in multiple copies. Applied topics with practical implications dominate.
The 20 papers concerning health-related issues are distributed across 20 unique domains, accounting for 46 URLs. The 27 papers dealing with environmental issues can be found across 26 unique domains, accounting for 56 URLs. Most of the identified papers exist in multiple copies and have already spread to several archives, repositories, and social media. It would be difficult, or impossible, to remove them from the scientific record.
As apparent from Table 2, GPT-fabricated, questionable papers are seeping into most parts of the online research infrastructure for scholarly communication. Platforms on which identified papers have appeared include ResearchGate, ORCiD, Journal of Population Therapeutics and Clinical Pharmacology (JPTCP), Easychair, Frontiers, the Institute of Electrical and Electronics Engineer (IEEE), and X/Twitter. Thus, even if they are retracted from their original source, it will prove very difficult to track, remove, or even just mark them up on other platforms. Moreover, unless regulated, Google Scholar will enable their continued and most likely unlabeled discoverability.
Environment | researchgate.net (13) | orcid.org (4) | easychair.org (3) | ijope.com* (3) | publikasiindonesia.id (3) |
Health | researchgate.net (15) | ieee.org (4) | twitter.com (3) | jptcp.com** (2) | frontiersin.org (2) |
A word rain visualization (Centre for Digital Humanities Uppsala, 2023), which combines word prominences through TF-IDF 5 Term frequency–inverse document frequency , a method for measuring the significance of a word in a document compared to its frequency across all documents in a collection. scores with semantic similarity of the full texts of our sample of GPT-generated articles that fall into the “Environment” and “Health” categories, reflects the two categories in question. However, as can be seen in Figure 1, it also reveals overlap and sub-areas. The y-axis shows word prominences through word positions and font sizes, while the x-axis indicates semantic similarity. In addition to a certain amount of overlap, this reveals sub-areas, which are best described as two distinct events within the word rain. The event on the left bundles terms related to the development and management of health and healthcare with “challenges,” “impact,” and “potential of artificial intelligence”emerging as semantically related terms. Terms related to research infrastructures, environmental, epistemic, and technological concepts are arranged further down in the same event (e.g., “system,” “climate,” “understanding,” “knowledge,” “learning,” “education,” “sustainable”). A second distinct event further to the right bundles terms associated with fish farming and aquatic medicinal plants, highlighting the presence of an aquaculture cluster. Here, the prominence of groups of terms such as “used,” “model,” “-based,” and “traditional” suggests the presence of applied research on these topics. The two events making up the word rain visualization, are linked by a less dominant but overlapping cluster of terms related to “energy” and “water.”
The bar chart of the terms in the paper subset (see Figure 2) complements the word rain visualization by depicting the most prominent terms in the full texts along the y-axis. Here, word prominences across health and environment papers are arranged descendingly, where values outside parentheses are TF-IDF values (relative frequencies) and values inside parentheses are raw term frequencies (absolute frequencies).
Finding 3: Google Scholar presents results from quality-controlled and non-controlled citation databases on the same interface, providing unfiltered access to GPT-fabricated questionable papers.
Google Scholar’s central position in the publicly accessible scholarly communication infrastructure, as well as its lack of standards, transparency, and accountability in terms of inclusion criteria, has potentially serious implications for public trust in science. This is likely to exacerbate the already-known potential to exploit Google Scholar for evidence hacking (Tripodi et al., 2023) and will have implications for any attempts to retract or remove fraudulent papers from their original publication venues. Any solution must consider the entirety of the research infrastructure for scholarly communication and the interplay of different actors, interests, and incentives.
We searched and scraped Google Scholar using the Python library Scholarly (Cholewiak et al., 2023) for papers that included specific phrases known to be common responses from ChatGPT and similar applications with the same underlying model (GPT3.5 or GPT4): “as of my last knowledge update” and/or “I don’t have access to real-time data” (see Appendix A). This facilitated the identification of papers that likely used generative AI to produce text, resulting in 227 retrieved papers. The papers’ bibliographic information was automatically added to a spreadsheet and downloaded into Zotero. 6 An open-source reference manager, https://zotero.org .
We employed multiple coding (Barbour, 2001) to classify the papers based on their content. First, we jointly assessed whether the paper was suspected of fraudulent use of ChatGPT (or similar) based on how the text was integrated into the papers and whether the paper was presented as original research output or the AI tool’s role was acknowledged. Second, in analyzing the content of the papers, we continued the multiple coding by classifying the fraudulent papers into four categories identified during an initial round of analysis—health, environment, computing, and others—and then determining which subjects were most affected by this issue (see Table 1). Out of the 227 retrieved papers, 88 papers were written with legitimate and/or declared use of GPTs (i.e., false positives, which were excluded from further analysis), and 139 papers were written with undeclared and/or fraudulent use (i.e., true positives, which were included in further analysis). The multiple coding was conducted jointly by all authors of the present article, who collaboratively coded and cross-checked each other’s interpretation of the data simultaneously in a shared spreadsheet file. This was done to single out coding discrepancies and settle coding disagreements, which in turn ensured methodological thoroughness and analytical consensus (see Barbour, 2001). Redoing the category coding later based on our established coding schedule, we achieved an intercoder reliability (Cohen’s kappa) of 0.806 after eradicating obvious differences.
The ranking algorithm of Google Scholar prioritizes highly cited and older publications (Martín-Martín et al., 2016). Therefore, the position of the articles on the search engine results pages was not particularly informative, considering the relatively small number of results in combination with the recency of the publications. Only the query “as of my last knowledge update” had more than two search engine result pages. On those, questionable articles with undeclared use of GPTs were evenly distributed across all result pages (min: 4, max: 9, mode: 8), with the proportion of undeclared use being slightly higher on average on later search result pages.
To understand how the papers making fraudulent use of generative AI were disseminated online, we programmatically searched for the paper titles (with exact string matching) in Google Search from our local IP address (see Appendix B) using the googlesearch – python library(Vikramaditya, 2020). We manually verified each search result to filter out false positives—results that were not related to the paper—and then compiled the most prominent URLs by field. This enabled the identification of other platforms through which the papers had been spread. We did not, however, investigate whether copies had spread into SciHub or other shadow libraries, or if they were referenced in Wikipedia.
We used descriptive statistics to count the prevalence of the number of GPT-fabricated papers across topics and venues and top domains by subject. The pandas software library for the Python programming language (The pandas development team, 2024) was used for this part of the analysis. Based on the multiple coding, paper occurrences were counted in relation to their categories, divided into indexed journals, non-indexed journals, student papers, and working papers. The schemes, subdomains, and subdirectories of the URL strings were filtered out while top-level domains and second-level domains were kept, which led to normalizing domain names. This, in turn, allowed the counting of domain frequencies in the environment and health categories. To distinguish word prominences and meanings in the environment and health-related GPT-fabricated questionable papers, a semantically-aware word cloud visualization was produced through the use of a word rain (Centre for Digital Humanities Uppsala, 2023) for full-text versions of the papers. Font size and y-axis positions indicate word prominences through TF-IDF scores for the environment and health papers (also visualized in a separate bar chart with raw term frequencies in parentheses), and words are positioned along the x-axis to reflect semantic similarity (Skeppstedt et al., 2024), with an English Word2vec skip gram model space (Fares et al., 2017). An English stop word list was used, along with a manually produced list including terms such as “https,” “volume,” or “years.”
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This research has been supported by Mistra, the Swedish Foundation for Strategic Environmental Research, through the research program Mistra Environmental Communication (Haider, Ekström, Rödl) and the Marcus and Amalia Wallenberg Foundation [2020.0004] (Söderström).
The authors declare no competing interests.
The research described in this article was carried out under Swedish legislation. According to the relevant EU and Swedish legislation (2003:460) on the ethical review of research involving humans (“Ethical Review Act”), the research reported on here is not subject to authorization by the Swedish Ethical Review Authority (“etikprövningsmyndigheten”) (SRC, 2017).
This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided that the original author and source are properly credited.
All data needed to replicate this study are available at the Harvard Dataverse: https://doi.org/10.7910/DVN/WUVD8X
The authors wish to thank two anonymous reviewers for their valuable comments on the article manuscript as well as the editorial group of Harvard Kennedy School (HKS) Misinformation Review for their thoughtful feedback and input.
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Research on the methods for correcting helicopter position on deck using a carrier robot.
2. mathematical model and kinematic characteristic analysis, 2.1. the structure and correction principle of helicopter deck traction system, 2.2. analysis of traction motion characteristics of srcr, 3. the stepwise method of helicopter deck position correction, 3.1. the principle of stepwise correction method, 3.2. the relationship of speed cooperative control in the stepwise correction method, 4. the continuous method of helicopter deck position correction, 4.1. the principle of continuous correction method, 4.1.1. parallel landing position, 4.1.2. cross landing position, 4.2. the relationship of speed cooperative control in the continuous correction method, 5. simulation verification of the correction method, 5.1. simulation of the stepwise correction method, 5.2. simulation of the continuous correction method, 5.3. comparative analysis of the efficiency of the stepwise correction method and the continuous correction method, 5.3.1. efficiency analysis of the stepwise correction method on the impact of initial yaw angle and traction speed, 5.3.2. efficiency analysis of the stepwise correction method on the impact of initial yaw angle and lateral offsets, 5.3.3. efficiency analysis of the continuous correction method on the impact of initial yaw angle and traction speed, 5.3.4. efficiency analysis of the continuous correction method on the impact of initial yaw angle and lateral offsets, 5.3.5. comparison of performance between stepwise and continuous correction methods, 6. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.
Click here to enlarge figure
Symbol | Description |
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θ | The helicopter fuselage yaw angle |
d | The helicopter lateral offset |
v | The traction velocity of the left winch |
v | The traction velocity of the right winch |
V | The traction speed provided by the left winch |
V | The traction speed provided by the right winch |
v | The velocity of the left traction point a of the SRCR |
v | The velocity of the right traction point b of the SRCR |
ω | The rotational angular velocity of the SRCR |
L | The distance between the left and right traction tracks |
Δv | The traction velocity difference between the left and right winches |
Δv | The velocity of the left traction point (shaft a) moving within the sliding groove |
Δv | The rotational velocity of the left traction point a on the SRCR when it performs rigid body motion around the right traction point b |
v | The capturing point velocity of the right robotic arm |
v | The lateral correction velocity of the right robotic arm |
r | The distance from the right robotic arm capturing point c to the right traction point b |
γ | The steering angle of the helicopter steerable nose wheel |
x | The sliding displacement of the right robotic arm |
L | The distance from the initial position of the right robotic arm (i.e., axis y ) to the center of the helicopter’s main wheel |
L | The distance between the right traction point of the SRCR (i.e., shaft b) and the initial position of the right robotic arm |
L | The distance between the helicopter main wheel shaft and the line connecting the left and right traction points (i.e., line a–b) |
v | The rotational velocity of the right robotic arm’s capture point around the right traction point of the SRCR (i.e., shaft b) |
δ | The central angle of the continuous correction trajectory |
r | The arc radius of the continuous correction trajectory |
L | The distance from the center of the helicopter’s main wheel shaft to the center of the helicopter steerable nose wheel |
s | The length of the continuous correction trajectory |
Helicopter Type | Width | Rear Wheelbase | Weight |
---|---|---|---|
CH-53K | 5.33 m | 3.52 m | 25,000 kg |
Sea hawk (SH-60B) | 2.36 m | 2.8 m | 10,400 kg |
Sea king (SH-3) | 4.98 m | 4.70 m | 9707 kg |
MI-171helicopter | 2.50 m | 4.28 m | 11,000 kg |
SH-90 | 3.8 m | 2.7 m | 10,600 kg |
Parameter | Value |
---|---|
The length of track spacing for left and right traction tracks L | 5000 mm |
The length of capturing point to fuselage center line L | 2000 mm |
The distance between the right traction point of the SRCR and the initial position of the right robotic arm L | 150 mm |
The distance between the helicopter main wheel shaft and the line connecting the left and right traction points L | 300 mm |
Initial helicopter yaw angle θ | 10° |
Initial helicopter lateral offset d | 200 mm |
Right traction speed v | 10 mm/s |
Parameter | Value |
---|---|
The length of track spacing for left and right traction L | 5000 mm |
The length of grab point to fuselage center line L | 2000 mm |
The distance between the right traction point of the SRCR and the initial position of the right robotic arm L | 150 mm |
The distance between the helicopter main wheel shaft and the line connecting the left and right traction points L | 300 mm |
The distance between helicopter control wheel and the main wheel shaft L | 8000 mm |
Initial helicopter yaw angle θ | −20° |
Initial helicopter lateral offset d | −200 mm |
Right traction speed v | 30 mm/s |
Helicopter control wheel angle γ | 67.48° |
Serial Number | Initial Fuselage Yaw Angle θ /deg | Lateral Offset d /mm | Right Traction Speed v /mm/s | Translation Time t /s | Rotation Time t /s | Total Correction Time t/s |
---|---|---|---|---|---|---|
1 | −10° | −200 | 10 | 108.71 | 43.80 | 152.51 |
2 | −10° | −200 | 20 | 54.56 | 21.80 | 76.36 |
3 | −10° | −200 | 30 | 36.42 | 14.60 | 51.02 |
4 | −20° | −200 | 10 | 78.29 | 91.00 | 169.29 |
5 | −20° | −200 | 20 | 39.18 | 45.60 | 84.78 |
6 | −20° | −200 | 30 | 26.16 | 30.40 | 56.56 |
7 | −30° | −200 | 10 | 95.56 | 149.20 | 244.76 |
8 | −30° | −200 | 20 | 47.82 | 74.60 | 122.42 |
9 | −30° | −200 | 30 | 31.90 | 49.80 | 81.70 |
Serial Number | Initial Fuselage Yaw Angle θ /deg | Lateral Offset d /mm | Right Traction Speed v /mm/s | Translation Time t /s | Rotation Time t /s | Total Correction Time t/s |
---|---|---|---|---|---|---|
1 | −10° | −600 | 30 | 109.26 | 14.60 | 123.86 |
2 | −10° | −400 | 30 | 72.84 | 14.60 | 87.44 |
3 | −10° | −200 | 30 | 36.42 | 14.60 | 51.02 |
4 | −10° | −150 | 30 | 27.32 | 14.60 | 41.92 |
5 | −10° | −100 | 30 | 18.21 | 14.60 | 32.81 |
6 | −10° | −50 | 30 | 9.11 | 14.60 | 23.71 |
7 | −20° | −600 | 30 | 78.48 | 30.40 | 108.88 |
8 | −20° | −400 | 30 | 52.32 | 30.40 | 82.72 |
9 | −20° | −200 | 30 | 26.16 | 30.40 | 56.56 |
10 | −20° | −150 | 30 | 19.62 | 30.40 | 50.02 |
11 | −20° | −100 | 30 | 13.08 | 30.40 | 43.48 |
12 | −30° | −600 | 30 | 95.70 | 49.80 | 145.50 |
13 | −30° | −400 | 30 | 63.80 | 49.80 | 113.60 |
14 | −30° | −200 | 30 | 31.90 | 49.80 | 81.70 |
15 | −30° | −150 | 30 | 23.88 | 49.80 | 73.68 |
16 | −30° | −100 | 30 | 15.85 | 49.80 | 65.65 |
Serial Number | Initial Fuselage Yaw Angle θ /deg | Lateral Offset d /mm | Right Traction Speed v /mm/s | Helicopter Control Wheel Angle γ/deg | Helicopter Correction Radius r/mm | Total Correction Time t/s |
---|---|---|---|---|---|---|
1 | −10° | −200 | 10 | 31.29 | 13162.86 | 271.60 |
2 | −10° | −200 | 20 | 31.29 | 13162.86 | 135.80 |
3 | −10° | −200 | 30 | 31.29 | 13162.86 | 90.60 |
4 | −20° | −200 | 10 | 67.48 | 3316.98 | 200.60 |
5 | −20° | −200 | 20 | 67.48 | 3316.98 | 100.40 |
6 | −20° | −200 | 30 | 67.48 | 3316.98 | 67.20 |
7 | −30° | −200 | 10 | 79.43 | 1492.82 | 207.60 |
8 | −30° | −200 | 20 | 79.43 | 1492.82 | 103.80 |
9 | −30° | −200 | 30 | 79.43 | 1492.82 | 69.20 |
Serial Number | Initial Fuselage Yaw Angle θ /deg | Lateral Offset d /mm | Right Traction Speed v /mm/s | Helicopter Control Wheel Angle γ/deg | Helicopter Correction Radius r/mm | Total Correction Time t/s |
---|---|---|---|---|---|---|
1 | −10° | −200 | 30 | 31.29 | 13162.86 | 90.60 |
2 | −10° | −150 | 30 | 39.01 | 9875.65 | 71.60 |
3 | −10° | −100 | 30 | 50.55 | 6582.97 | 52.80 |
4 | −10° | −50 | 30 | 67.63 | 3292.46 | 33.80 |
5 | −20° | −200 | 30 | 67.48 | 3316.98 | 67.00 |
6 | −20° | −150 | 30 | 72.73 | 2487.13 | 57.60 |
7 | −20° | −100 | 30 | 78.29 | 1658.18 | 48.40 |
8 | −30° | −200 | 30 | 79.43 | 1492.82 | 69.20 |
9 | −30° | −150 | 30 | 82.03 | 1120.06 | 63.40 |
10 | −30° | −100 | 30 | 84.67 | 746.36 | 57.40 |
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Zhong, Y.; Zhao, D.; Zhao, X. Research on the Methods for Correcting Helicopter Position on Deck Using a Carrier Robot. Actuators 2024 , 13 , 342. https://doi.org/10.3390/act13090342
Zhong Y, Zhao D, Zhao X. Research on the Methods for Correcting Helicopter Position on Deck Using a Carrier Robot. Actuators . 2024; 13(9):342. https://doi.org/10.3390/act13090342
Zhong, Yuhang, Dingxuan Zhao, and Xiaolong Zhao. 2024. "Research on the Methods for Correcting Helicopter Position on Deck Using a Carrier Robot" Actuators 13, no. 9: 342. https://doi.org/10.3390/act13090342
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Methodology
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.
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.
Approach | What does it involve? |
---|---|
Grounded theory | Researchers collect rich data on a topic of interest and develop theories . |
Researchers immerse themselves in groups or organizations to understand their cultures. | |
Action research | Researchers and participants collaboratively link theory to practice to drive social change. |
Phenomenological research | Researchers investigate a phenomenon or event by describing and interpreting participants’ lived experiences. |
Narrative research | Researchers examine how stories are told to understand how participants perceive and make sense of their experiences. |
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:
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:
There are several specific approaches to analyzing qualitative data. Although these methods share similar processes, they emphasize different concepts.
Approach | When to use | Example |
---|---|---|
To describe and categorize common words, phrases, and ideas in qualitative data. | A market researcher could perform content analysis to find out what kind of language is used in descriptions of therapeutic apps. | |
To identify and interpret patterns and themes in qualitative data. | A psychologist could apply thematic analysis to travel blogs to explore how tourism shapes self-identity. | |
To examine the content, structure, and design of texts. | A media researcher could use textual analysis to understand how news coverage of celebrities has changed in the past decade. | |
To study communication and how language is used to achieve effects in specific contexts. | A political scientist could use discourse analysis to study how politicians generate trust in election campaigns. |
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:
The data collection and analysis process can be adapted as new ideas or patterns emerge. They are not rigidly decided beforehand.
Data collection occurs in real-world contexts or in naturalistic ways.
Detailed descriptions of people’s experiences, feelings and perceptions can be used in designing, testing or improving systems or products.
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:
The real-world setting often makes qualitative research unreliable because of uncontrolled factors that affect the data.
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.
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 .
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.
Research 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 :
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:
The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .
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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.
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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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.
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.
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.
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
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).
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).
Main evaluations performed to guide the return to sport following sport-related concussion
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.
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.
The patients or the public were not involved in the design, conduct, reporting or dissemination plans of our research.
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.
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).
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%).
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
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
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.
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.
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
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
Patient consent for publication.
Not applicable.
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.
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.
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.
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.
Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.
Competing interests None declared.
Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.
Provenance and peer review Not commissioned; externally peer reviewed.
Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.
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Reading Time: 14 minutes In this article I will show you how to write a research paper using the four LEAP writing steps. The LEAP academic writing approach is a step-by-step method for turning research results into a published paper.. The LEAP writing approach has been the cornerstone of the 70 + research papers that I have authored and the 3700+ citations these paper have accumulated within ...
Research Methods | Definitions, Types, Examples
By refining your focus, you can produce a thoughtful and engaging paper that effectively communicates your ideas to your readers. 5. Write a thesis statement. A thesis statement is a one-to-two-sentence summary of your research paper's main argument or direction.
The methods section describes actions taken to investigate a research problem and the rationale for the application of specific procedures or techniques used to identify, select, process, and analyze information applied to understanding the problem, thereby, allowing the reader to critically evaluate a study's overall validity and reliability.
Definition: Research Paper is a written document that presents the author's original research, analysis, and interpretation of a specific topic or issue. It is typically based on Empirical Evidence, and may involve qualitative or quantitative research methods, or a combination of both. The purpose of a research paper is to contribute new ...
Your Methods Section contextualizes the results of your study, giving editors, reviewers and readers alike the information they need to understand and interpret your work. Your methods are key to establishing the credibility of your study, along with your data and the results themselves. A complete methods section should provide enough detail for a skilled researcher to replicate your process ...
What Is a Research Methodology? | Steps & Tips. Published on August 25, 2022 by Shona McCombes and Tegan George. Revised on November 20, 2023. Your research methodology discusses and explains the data collection and analysis methods you used in your research. A key part of your thesis, dissertation, or research paper, the methodology chapter explains what you did and how you did it, allowing ...
Writing the Research Paper. Write a detailed outline. Almost the rough content of every paragraph. The order of the various topics in your paper. On the basis of the outline, start writing a part by planning the content, and then write it down. Put a visible mark (which you will later delete) where you need to quote a source, and write in the ...
1. Qualitative research methodology. Qualitative research methodology is aimed at understanding concepts, thoughts, or experiences. This approach is descriptive and is often utilized to gather in-depth insights into people's attitudes, behaviors, or cultures. Qualitative research methodology involves methods like interviews, focus groups, and ...
How to Write a Research Paper | A Beginner's Guide
The research methodology is an important section of any research paper or thesis, as it describes the methods and procedures that will be used to conduct the research. It should include details about the research design, data collection methods, data analysis techniques, and any ethical considerations.
Research methods are specific procedures for collecting and analysing data. Developing your research methods is an integral part of your research design. When planning your methods, there are two key decisions you will make. First, decide how you will collect data. Your methods depend on what type of data you need to answer your research question:
How to Write an Outline for Your Research Paper. There is no "one size fits all" outlining technique. Some students might devote all their energy and attention to the outline in order to avoid the paper. Other students may benefit from being made to sit down and organize their thoughts into a lengthy sentence outline.
Examples of quantitative research techniques are surveys, experiments, and statistical analysis. Qualitative research: This is a research method that focuses on collecting and analyzing non-numerical data, such as text, images, and videos, to gain insights into the subjective experiences and perspectives of the participants.
The Ultimate Guide to Writing a Research Paper
The methods section is a fundamental section of any paper since it typically discusses the 'what', 'how', 'which', and 'why' of the study, which is necessary to arrive at the final conclusions. In a research article, the introduction, which serves to set the foundation for comprehending the background and results is usually ...
What is Research Methodology? Definition, Types, and ...
The methods section of a research paper provides the information by which a study's validity is judged. Therefore, it requires a clear and precise description of how an experiment was done, and the rationale for why specific experimental procedures were chosen. The methods section should describe what was
How to Write an APA Methods Section | With Examples
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Quantitative research methods are used to collect and analyze numerical data. This type of research is useful when the objective is to test a hypothesis, determine cause-and-effect relationships, and measure the prevalence of certain phenomena. Quantitative research methods include surveys, experiments, and secondary data analysis.
Writing a Research Paper Introduction | Step-by-Step Guide
Academic journals, archives, and repositories are seeing an increasing number of questionable research papers clearly produced using generative AI. They are often created with widely available, general-purpose AI applications, most likely ChatGPT, and mimic scientific writing. Google Scholar easily locates and lists these questionable papers alongside reputable, quality-controlled research.
Feature papers represent the most advanced research with significant potential for high impact in the field. A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and describes possible research applications.
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 ...
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 ...