research strategies paper

Writing a Research Strategy

This page is focused on providing practical tips and suggestions for preparing The Research Strategy, the primary component of an application's Research Plan along with the Specific Aims. The guidance on this page is primarily geared towards an R01-style application, however, much of it is useful for other grant types as well.

Developing the Research Strategy

The primary audience for your application is your peer review group. When writing your Research Strategy, your goal is to present a well-organized, visually appealing, and readable description of your proposed project and the rationale for pursuing it. Your writing should be streamlined and organized so your reviewers can readily grasp the information. If it's a key point, repeat it, then repeat it again. Add more emphasis by putting the text in bold , or bold italics . If writing is not your forte, get help. For more information, please visit  W riting For Reviewers .

How to Organize the Research Strategy Section

How to organize a Research Strategy is largely up to the applicant. Start by following the NIH application instructions and guidelines for formatting attachments such as the research plan section.

It is generally structured as follows:

Significance

For Preliminary Studies (for new applications) or a Progress Report (for renewal and revision applications).

• You can either include preliminary studies or progress report information as a subsection of Approach or integrate it into any or all of the three main sections.
• If you do the latter, be sure to mark the information clearly, for example, with a bold subhead.

 Helpful tips to consider when formatting:

• Organize using bold headers or an outline or numbering system—or both—that are used consistently throughout.
• Start each section with the appropriate header: Significance, Innovation, or Approach.
• Organize the Approach section around the Specific Aims.

For most applications, you need to address Rigor ous Study Design  by describing the experimental design and methods you propose and how they will achieve robust and unbiased results. See the NIH guidance for elaboration on the 4 major areas of rigor and transparency emphasized in grant review. These requirements apply to research grant, career development, fellowship, and training applications.

Tips for Drafting Sections of the Research Strategy

Although you will emphasize your project's significance throughout the application, the Significance section should give the most details. The farther removed your reviewers are from your field, the more information you'll need to provide on basic biology, importance of the area, research opportunities, and new findings. Reviewing the potentially relevant study section rosters may give you some ideas as to general reviewer expertise. You will also need to describe the prior and preliminary studies that provide a strong scientific rationale for pursuing the proposed studies, emphasizing the strengths and weaknesses in the rigor and transparency of these key studies.

This section gives you the chance to explain how your application is conceptually and/or technically innovative. Some examples as to how you might do this could include but not limited to:

• Demonstrate the proposed research is new and unique, e.g., explores new scientific avenues, has a novel hypothesis, will create new knowledge.
• Explain how the proposed work can refine, improve, or propose a new application of an existing concept or method.

If your proposal is paradigm-shifting or challenges commonly held beliefs, be sure that you include sufficient evidence in your preliminary data to convince reviewers, including strong rationale, data supporting the approach, and clear feasibility. Your job is to make the reviewers feel confident that the risk is worth taking.

For projects predominantly focused on innovation and outside-the-box research, investigators may wish to consider mechanisms other than R01s for example (e.g., exploratory/developmental research (R21) grants, NIH Director's Pioneer Award Program (DP1), and NIH Director's New Innovator Award Program (DP2).

The Approach section is where the experimental design is described. Expect your assigned reviewers to scrutinize your approach: they will want to know what you plan to do, how you plan to do it, and whether you can do it. NIH data show that of the peer review criteria, approach has the highest correlation with the overall impact score. Importantly, elements of rigorous study design should be addressed in this section, such as plans for minimization of bias (e.g. methods for blinding and treatment randomization) and consideration of relevant biological variables. Likewise, be sure to lay out a plan for alternative experiments and approaches in case you get uninterpretable or surprising results, and also consider limitations of the study and alternative interpretations. Point out any procedures, situations, or materials that may be hazardous to personnel and precautions to be exercised. A full discussion on the use of select agents should appear in the Select Agent Research attachment. Consider including a timeline demonstrating anticipated completion of the Aims. 

Here are some pointers to consider when organizing your Approach section:

• Enter a bold header for each Specific Aim.
• Under each aim, describe the experiments.
• If you get result X, you will follow pathway X; if you get result Y, you will follow pathway Y.
• Consider illustrating this with a flowchart.

Preliminary Studies

If submitting a new application to a NOFO that allows preliminary data, it is strongly encouraged to include preliminary studies. Preliminary studies demonstrate competency in the methods and interpretation. Well-designed and robust preliminary studies also serve to provide a strong scientific rationale for the proposed follow-up experiments. Reviewers also use preliminary studies together with the biosketches to assess the investigator review criterion, which reflects the competence of the research team. Provide alternative interpretations to your data to show reviewers you've thought through problems in-depth and are prepared to meet future challenges. As noted above, preliminary data can be put anywhere in the Research Strategy, but just make sure reviewers will be able to distinguish it from the proposed studies. Alternatively, it can be a separate section with its own header.

Progress Reports

If applying for a renewal or a revision (a competing supplement to an existing grant), include a progress report for reviewers.

Create a header so reviewers can easily find it and include the following information:

• Project period beginning and end dates.
• Summary of the importance and robustness of the completed findings in relation to the Specific Aims.
• Account of published and unpublished results, highlighting progress toward achieving your Specific Aims.

Other Helpful Tips

Referencing publications.

References show breadth of knowledge of the field and provide a scientific foundation for your application. If a critical work is omitted, reviewers may assume the applicant is not aware of it or deliberately ignoring it.

Throughout the application, reference all relevant publications for the concepts underlying your research and your methods. Remember the strengths and weaknesses in the rigor of the key studies you cite for justifying your proposal will need to be discussed in the Significance and/or Approach sections.

Read more about Bibliography and References Cited at Additional Application Elements .

Graphics can illustrate complex information in a small space and add visual interest to your application. Including schematics, tables, illustrations, graphs, and other types of graphics can enhance applications. Consider adding a timetable or flowchart to illustrate your experimental plan, including decision trees with alternative experimental pathways to help your reviewers understand your plans.

Video may enhance your application beyond what graphics alone can achieve. If you plan to send one or more videos, you'll need to meet certain requirements and include key information in your Research Strategy. State in your cover letter that a video will be included in your application (don't attach your files to the application). After you apply and get assignment information from the Commons, ask your assigned Scientific Review Officer (SRO) how your business official should send the files. Your video files are due at least one month before the peer review meeting.

However, you can't count on all reviewers being able to see or hear video, so you'll want to be strategic in how you incorporate it into your application by taking the following steps:

• Caption any narration in the video.
• Include key images from the video
• Write a description of the video, so the text would make sense even without the video.

Tracking for Your Budget

As you design your experiments, keep a running tab of the following essential data:

• Who. A list of people who will help (for the Key Personnel section later).
• What. A list of equipment and supplies for the experiments
• Time. Notes on how long each step takes. Timing directly affects the budget as well as how many Specific Aims can realistically be achieved.

Jotting this information down will help when Creating a Budget  and complete other sections later.

Review and Finalize Your Research Plan

Critically review the research plan through the lens of a reviewer to identify potential questions or weak spots.

Enlist others to review your application with a fresh eye. Include people who aren't familiar with the research to make sure the proposed work is clear to someone outside the field.

When finalizing the details of the Research Strategy, revisit and revise the Specific Aims as needed. Please see Writing Specific Aims . 

Want to contact NINDS staff? Please visit our Find Your NINDS Program Officer page to learn more about contacting Program Officer, Grants Management Specialists, Scientific Review Officers, and Health Program Specialists. Find NINDS Program Officer

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Organizing Your Social Sciences Research Paper

• 6. The Methodology
• Purpose of Guide
• Design Flaws to Avoid
• Independent and Dependent Variables
• Glossary of Research Terms
• Reading Research Effectively
• Narrowing a Topic Idea
• Broadening a Topic Idea
• Extending the Timeliness of a Topic Idea
• Academic Writing Style
• Applying Critical Thinking
• Choosing a Title
• Making an Outline
• Paragraph Development
• Research Process Video Series
• Executive Summary
• The C.A.R.S. Model
• Background Information
• The Research Problem/Question
• Theoretical Framework
• Citation Tracking
• Content Alert Services
• Evaluating Sources
• Primary Sources
• Secondary Sources
• Tiertiary Sources
• Scholarly vs. Popular Publications
• Qualitative Methods
• Quantitative Methods
• Insiderness
• Using Non-Textual Elements
• Limitations of the Study
• Common Grammar Mistakes
• Writing Concisely
• Avoiding Plagiarism
• Footnotes or Endnotes?
• Further Readings
• Generative AI and Writing
• USC Libraries Tutorials and Other Guides
• Bibliography

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. The methodology section of a research paper answers two main questions: How was the data collected or generated? And, how was it analyzed? The writing should be direct and precise and always written in the past tense.

Kallet, Richard H. "How to Write the Methods Section of a Research Paper." Respiratory Care 49 (October 2004): 1229-1232.

Importance of a Good Methodology Section

You must explain how you obtained and analyzed your results for the following reasons:

• Readers need to know how the data was obtained because the method you chose affects the results and, by extension, how you interpreted their significance in the discussion section of your paper.
• Methodology is crucial for any branch of scholarship because an unreliable method produces unreliable results and, as a consequence, undermines the value of your analysis of the findings.
• In most cases, there are a variety of different methods you can choose to investigate a research problem. The methodology section of your paper should clearly articulate the reasons why you have chosen a particular procedure or technique.
• The reader wants to know that the data was collected or generated in a way that is consistent with accepted practice in the field of study. For example, if you are using a multiple choice questionnaire, readers need to know that it offered your respondents a reasonable range of answers to choose from.
• The method must be appropriate to fulfilling the overall aims of the study. For example, you need to ensure that you have a large enough sample size to be able to generalize and make recommendations based upon the findings.
• The methodology should discuss the problems that were anticipated and the steps you took to prevent them from occurring. For any problems that do arise, you must describe the ways in which they were minimized or why these problems do not impact in any meaningful way your interpretation of the findings.
• In the social and behavioral sciences, it is important to always provide sufficient information to allow other researchers to adopt or replicate your methodology. This information is particularly important when a new method has been developed or an innovative use of an existing method is utilized.

Bem, Daryl J. Writing the Empirical Journal Article. Psychology Writing Center. University of Washington; Denscombe, Martyn. The Good Research Guide: For Small-Scale Social Research Projects . 5th edition. Buckingham, UK: Open University Press, 2014; Lunenburg, Frederick C. Writing a Successful Thesis or Dissertation: Tips and Strategies for Students in the Social and Behavioral Sciences . Thousand Oaks, CA: Corwin Press, 2008.

Structure and Writing Style

I.  Groups of Research Methods

There are two main groups of research methods in the social sciences:

• The e mpirical-analytical group approaches the study of social sciences in a similar manner that researchers study the natural sciences . This type of research focuses on objective knowledge, research questions that can be answered yes or no, and operational definitions of variables to be measured. The empirical-analytical group employs deductive reasoning that uses existing theory as a foundation for formulating hypotheses that need to be tested. This approach is focused on explanation.
• The i nterpretative group of methods is focused on understanding phenomenon in a comprehensive, holistic way . Interpretive methods focus on analytically disclosing the meaning-making practices of human subjects [the why, how, or by what means people do what they do], while showing how those practices arrange so that it can be used to generate observable outcomes. Interpretive methods allow you to recognize your connection to the phenomena under investigation. However, the interpretative group requires careful examination of variables because it focuses more on subjective knowledge.

II.  Content

The introduction to your methodology section should begin by restating the research problem and underlying assumptions underpinning your study. This is followed by situating the methods you used to gather, analyze, and process information within the overall “tradition” of your field of study and within the particular research design you have chosen to study the problem. If the method you choose lies outside of the tradition of your field [i.e., your review of the literature demonstrates that the method is not commonly used], provide a justification for how your choice of methods specifically addresses the research problem in ways that have not been utilized in prior studies.

The remainder of your methodology section should describe the following:

• Decisions made in selecting the data you have analyzed or, in the case of qualitative research, the subjects and research setting you have examined,
• Tools and methods used to identify and collect information, and how you identified relevant variables,
• The ways in which you processed the data and the procedures you used to analyze that data, and
• The specific research tools or strategies that you utilized to study the underlying hypothesis and research questions.

In addition, an effectively written methodology section should:

• Introduce the overall methodological approach for investigating your research problem . Is your study qualitative or quantitative or a combination of both (mixed method)? Are you going to take a special approach, such as action research, or a more neutral stance?
• Indicate how the approach fits the overall research design . Your methods for gathering data should have a clear connection to your research problem. In other words, make sure that your methods will actually address the problem. One of the most common deficiencies found in research papers is that the proposed methodology is not suitable to achieving the stated objective of your paper.
• Describe the specific methods of data collection you are going to use , such as, surveys, interviews, questionnaires, observation, archival research. If you are analyzing existing data, such as a data set or archival documents, describe how it was originally created or gathered and by whom. Also be sure to explain how older data is still relevant to investigating the current research problem.
• Explain how you intend to analyze your results . Will you use statistical analysis? Will you use specific theoretical perspectives to help you analyze a text or explain observed behaviors? Describe how you plan to obtain an accurate assessment of relationships, patterns, trends, distributions, and possible contradictions found in the data.
• Provide background and a rationale for methodologies that are unfamiliar for your readers . Very often in the social sciences, research problems and the methods for investigating them require more explanation/rationale than widely accepted rules governing the natural and physical sciences. Be clear and concise in your explanation.
• Provide a justification for subject selection and sampling procedure . For instance, if you propose to conduct interviews, how do you intend to select the sample population? If you are analyzing texts, which texts have you chosen, and why? If you are using statistics, why is this set of data being used? If other data sources exist, explain why the data you chose is most appropriate to addressing the research problem.
• Provide a justification for case study selection . A common method of analyzing research problems in the social sciences is to analyze specific cases. These can be a person, place, event, phenomenon, or other type of subject of analysis that are either examined as a singular topic of in-depth investigation or multiple topics of investigation studied for the purpose of comparing or contrasting findings. In either method, you should explain why a case or cases were chosen and how they specifically relate to the research problem.
• Describe potential limitations . Are there any practical limitations that could affect your data collection? How will you attempt to control for potential confounding variables and errors? If your methodology may lead to problems you can anticipate, state this openly and show why pursuing this methodology outweighs the risk of these problems cropping up.

NOTE:   Once you have written all of the elements of the methods section, subsequent revisions should focus on how to present those elements as clearly and as logically as possibly. The description of how you prepared to study the research problem, how you gathered the data, and the protocol for analyzing the data should be organized chronologically. For clarity, when a large amount of detail must be presented, information should be presented in sub-sections according to topic. If necessary, consider using appendices for raw data.

ANOTHER NOTE: If you are conducting a qualitative analysis of a research problem , the methodology section generally requires a more elaborate description of the methods used as well as an explanation of the processes applied to gathering and analyzing of data than is generally required for studies using quantitative methods. Because you are the primary instrument for generating the data [e.g., through interviews or observations], the process for collecting that data has a significantly greater impact on producing the findings. Therefore, qualitative research requires a more detailed description of the methods used.

YET ANOTHER NOTE:   If your study involves interviews, observations, or other qualitative techniques involving human subjects , you may be required to obtain approval from the university's Office for the Protection of Research Subjects before beginning your research. This is not a common procedure for most undergraduate level student research assignments. However, i f your professor states you need approval, you must include a statement in your methods section that you received official endorsement and adequate informed consent from the office and that there was a clear assessment and minimization of risks to participants and to the university. This statement informs the reader that your study was conducted in an ethical and responsible manner. In some cases, the approval notice is included as an appendix to your paper.

III.  Problems to Avoid

Irrelevant Detail The methodology section of your paper should be thorough but concise. Do not provide any background information that does not directly help the reader understand why a particular method was chosen, how the data was gathered or obtained, and how the data was analyzed in relation to the research problem [note: analyzed, not interpreted! Save how you interpreted the findings for the discussion section]. With this in mind, the page length of your methods section will generally be less than any other section of your paper except the conclusion.

Unnecessary Explanation of Basic Procedures Remember that you are not writing a how-to guide about a particular method. You should make the assumption that readers possess a basic understanding of how to investigate the research problem on their own and, therefore, you do not have to go into great detail about specific methodological procedures. The focus should be on how you applied a method , not on the mechanics of doing a method. An exception to this rule is if you select an unconventional methodological approach; if this is the case, be sure to explain why this approach was chosen and how it enhances the overall process of discovery.

Problem Blindness It is almost a given that you will encounter problems when collecting or generating your data, or, gaps will exist in existing data or archival materials. Do not ignore these problems or pretend they did not occur. Often, documenting how you overcame obstacles can form an interesting part of the methodology. It demonstrates to the reader that you can provide a cogent rationale for the decisions you made to minimize the impact of any problems that arose.

Literature Review Just as the literature review section of your paper provides an overview of sources you have examined while researching a particular topic, the methodology section should cite any sources that informed your choice and application of a particular method [i.e., the choice of a survey should include any citations to the works you used to help construct the survey].

It’s More than Sources of Information! A description of a research study's method should not be confused with a description of the sources of information. Such a list of sources is useful in and of itself, especially if it is accompanied by an explanation about the selection and use of the sources. The description of the project's methodology complements a list of sources in that it sets forth the organization and interpretation of information emanating from those sources.

Azevedo, L.F. et al. "How to Write a Scientific Paper: Writing the Methods Section." Revista Portuguesa de Pneumologia 17 (2011): 232-238; Blair Lorrie. “Choosing a Methodology.” In Writing a Graduate Thesis or Dissertation , Teaching Writing Series. (Rotterdam: Sense Publishers 2016), pp. 49-72; Butin, Dan W. The Education Dissertation A Guide for Practitioner Scholars . Thousand Oaks, CA: Corwin, 2010; Carter, Susan. Structuring Your Research Thesis . New York: Palgrave Macmillan, 2012; Kallet, Richard H. “How to Write the Methods Section of a Research Paper.” Respiratory Care 49 (October 2004):1229-1232; Lunenburg, Frederick C. Writing a Successful Thesis or Dissertation: Tips and Strategies for Students in the Social and Behavioral Sciences . Thousand Oaks, CA: Corwin Press, 2008. Methods Section. The Writer’s Handbook. Writing Center. University of Wisconsin, Madison; Rudestam, Kjell Erik and Rae R. Newton. “The Method Chapter: Describing Your Research Plan.” In Surviving Your Dissertation: A Comprehensive Guide to Content and Process . (Thousand Oaks, Sage Publications, 2015), pp. 87-115; What is Interpretive Research. Institute of Public and International Affairs, University of Utah; Writing the Experimental Report: Methods, Results, and Discussion. The Writing Lab and The OWL. Purdue University; Methods and Materials. The Structure, Format, Content, and Style of a Journal-Style Scientific Paper. Department of Biology. Bates College.

Writing Tip

Statistical Designs and Tests? Do Not Fear Them!

Don't avoid using a quantitative approach to analyzing your research problem just because you fear the idea of applying statistical designs and tests. A qualitative approach, such as conducting interviews or content analysis of archival texts, can yield exciting new insights about a research problem, but it should not be undertaken simply because you have a disdain for running a simple regression. A well designed quantitative research study can often be accomplished in very clear and direct ways, whereas, a similar study of a qualitative nature usually requires considerable time to analyze large volumes of data and a tremendous burden to create new paths for analysis where previously no path associated with your research problem had existed.

To locate data and statistics, GO HERE .

Another Writing Tip

Knowing the Relationship Between Theories and Methods

There can be multiple meaning associated with the term "theories" and the term "methods" in social sciences research. A helpful way to delineate between them is to understand "theories" as representing different ways of characterizing the social world when you research it and "methods" as representing different ways of generating and analyzing data about that social world. Framed in this way, all empirical social sciences research involves theories and methods, whether they are stated explicitly or not. However, while theories and methods are often related, it is important that, as a researcher, you deliberately separate them in order to avoid your theories playing a disproportionate role in shaping what outcomes your chosen methods produce.

Introspectively engage in an ongoing dialectic between the application of theories and methods to help enable you to use the outcomes from your methods to interrogate and develop new theories, or ways of framing conceptually the research problem. This is how scholarship grows and branches out into new intellectual territory.

Reynolds, R. Larry. Ways of Knowing. Alternative Microeconomics . Part 1, Chapter 3. Boise State University; The Theory-Method Relationship. S-Cool Revision. United Kingdom.

Yet Another Writing Tip

Methods and the Methodology

Do not confuse the terms "methods" and "methodology." As Schneider notes, a method refers to the technical steps taken to do research . Descriptions of methods usually include defining and stating why you have chosen specific techniques to investigate a research problem, followed by an outline of the procedures you used to systematically select, gather, and process the data [remember to always save the interpretation of data for the discussion section of your paper].

The methodology refers to a discussion of the underlying reasoning why particular methods were used . This discussion includes describing the theoretical concepts that inform the choice of methods to be applied, placing the choice of methods within the more general nature of academic work, and reviewing its relevance to examining the research problem. The methodology section also includes a thorough review of the methods other scholars have used to study the topic.

Bryman, Alan. "Of Methods and Methodology." Qualitative Research in Organizations and Management: An International Journal 3 (2008): 159-168; Schneider, Florian. “What's in a Methodology: The Difference between Method, Methodology, and Theory…and How to Get the Balance Right?” PoliticsEastAsia.com. Chinese Department, University of Leiden, Netherlands.

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Focus: Education — Career Advice

How to write your first research paper.

Writing a research manuscript is an intimidating process for many novice writers in the sciences. One of the stumbling blocks is the beginning of the process and creating the first draft. This paper presents guidelines on how to initiate the writing process and draft each section of a research manuscript. The paper discusses seven rules that allow the writer to prepare a well-structured and comprehensive manuscript for a publication submission. In addition, the author lists different strategies for successful revision. Each of those strategies represents a step in the revision process and should help the writer improve the quality of the manuscript. The paper could be considered a brief manual for publication.

It is late at night. You have been struggling with your project for a year. You generated an enormous amount of interesting data. Your pipette feels like an extension of your hand, and running western blots has become part of your daily routine, similar to brushing your teeth. Your colleagues think you are ready to write a paper, and your lab mates tease you about your “slow” writing progress. Yet days pass, and you cannot force yourself to sit down to write. You have not written anything for a while (lab reports do not count), and you feel you have lost your stamina. How does the writing process work? How can you fit your writing into a daily schedule packed with experiments? What section should you start with? What distinguishes a good research paper from a bad one? How should you revise your paper? These and many other questions buzz in your head and keep you stressed. As a result, you procrastinate. In this paper, I will discuss the issues related to the writing process of a scientific paper. Specifically, I will focus on the best approaches to start a scientific paper, tips for writing each section, and the best revision strategies.

1. Schedule your writing time in Outlook

Whether you have written 100 papers or you are struggling with your first, starting the process is the most difficult part unless you have a rigid writing schedule. Writing is hard. It is a very difficult process of intense concentration and brain work. As stated in Hayes’ framework for the study of writing: “It is a generative activity requiring motivation, and it is an intellectual activity requiring cognitive processes and memory” [ 1 ]. In his book How to Write a Lot: A Practical Guide to Productive Academic Writing , Paul Silvia says that for some, “it’s easier to embalm the dead than to write an article about it” [ 2 ]. Just as with any type of hard work, you will not succeed unless you practice regularly. If you have not done physical exercises for a year, only regular workouts can get you into good shape again. The same kind of regular exercises, or I call them “writing sessions,” are required to be a productive author. Choose from 1- to 2-hour blocks in your daily work schedule and consider them as non-cancellable appointments. When figuring out which blocks of time will be set for writing, you should select the time that works best for this type of work. For many people, mornings are more productive. One Yale University graduate student spent a semester writing from 8 a.m. to 9 a.m. when her lab was empty. At the end of the semester, she was amazed at how much she accomplished without even interrupting her regular lab hours. In addition, doing the hardest task first thing in the morning contributes to the sense of accomplishment during the rest of the day. This positive feeling spills over into our work and life and has a very positive effect on our overall attitude.

Rule 1: Create regular time blocks for writing as appointments in your calendar and keep these appointments.

2. start with an outline.

Now that you have scheduled time, you need to decide how to start writing. The best strategy is to start with an outline. This will not be an outline that you are used to, with Roman numerals for each section and neat parallel listing of topic sentences and supporting points. This outline will be similar to a template for your paper. Initially, the outline will form a structure for your paper; it will help generate ideas and formulate hypotheses. Following the advice of George M. Whitesides, “. . . start with a blank piece of paper, and write down, in any order, all important ideas that occur to you concerning the paper” [ 3 ]. Use Table 1 as a starting point for your outline. Include your visuals (figures, tables, formulas, equations, and algorithms), and list your findings. These will constitute the first level of your outline, which will eventually expand as you elaborate.

The next stage is to add context and structure. Here you will group all your ideas into sections: Introduction, Methods, Results, and Discussion/Conclusion ( Table 2 ). This step will help add coherence to your work and sift your ideas.

Now that you have expanded your outline, you are ready for the next step: discussing the ideas for your paper with your colleagues and mentor. Many universities have a writing center where graduate students can schedule individual consultations and receive assistance with their paper drafts. Getting feedback during early stages of your draft can save a lot of time. Talking through ideas allows people to conceptualize and organize thoughts to find their direction without wasting time on unnecessary writing. Outlining is the most effective way of communicating your ideas and exchanging thoughts. Moreover, it is also the best stage to decide to which publication you will submit the paper. Many people come up with three choices and discuss them with their mentors and colleagues. Having a list of journal priorities can help you quickly resubmit your paper if your paper is rejected.

Rule 2: Create a detailed outline and discuss it with your mentor and peers.

3. continue with drafts.

After you get enough feedback and decide on the journal you will submit to, the process of real writing begins. Copy your outline into a separate file and expand on each of the points, adding data and elaborating on the details. When you create the first draft, do not succumb to the temptation of editing. Do not slow down to choose a better word or better phrase; do not halt to improve your sentence structure. Pour your ideas into the paper and leave revision and editing for later. As Paul Silvia explains, “Revising while you generate text is like drinking decaffeinated coffee in the early morning: noble idea, wrong time” [ 2 ].

Many students complain that they are not productive writers because they experience writer’s block. Staring at an empty screen is frustrating, but your screen is not really empty: You have a template of your article, and all you need to do is fill in the blanks. Indeed, writer’s block is a logical fallacy for a scientist ― it is just an excuse to procrastinate. When scientists start writing a research paper, they already have their files with data, lab notes with materials and experimental designs, some visuals, and tables with results. All they need to do is scrutinize these pieces and put them together into a comprehensive paper.

3.1. Starting with Materials and Methods

If you still struggle with starting a paper, then write the Materials and Methods section first. Since you have all your notes, it should not be problematic for you to describe the experimental design and procedures. Your most important goal in this section is to be as explicit as possible by providing enough detail and references. In the end, the purpose of this section is to allow other researchers to evaluate and repeat your work. So do not run into the same problems as the writers of the sentences in (1):

1a. Bacteria were pelleted by centrifugation. 1b. To isolate T cells, lymph nodes were collected.

As you can see, crucial pieces of information are missing: the speed of centrifuging your bacteria, the time, and the temperature in (1a); the source of lymph nodes for collection in (b). The sentences can be improved when information is added, as in (2a) and (2b), respectfully:

2a. Bacteria were pelleted by centrifugation at 3000g for 15 min at 25°C. 2b. To isolate T cells, mediastinal and mesenteric lymph nodes from Balb/c mice were collected at day 7 after immunization with ovabumin.

If your method has previously been published and is well-known, then you should provide only the literature reference, as in (3a). If your method is unpublished, then you need to make sure you provide all essential details, as in (3b).

3a. Stem cells were isolated, according to Johnson [23]. 3b. Stem cells were isolated using biotinylated carbon nanotubes coated with anti-CD34 antibodies.

Furthermore, cohesion and fluency are crucial in this section. One of the malpractices resulting in disrupted fluency is switching from passive voice to active and vice versa within the same paragraph, as shown in (4). This switching misleads and distracts the reader.

4. Behavioral computer-based experiments of Study 1 were programmed by using E-Prime. We took ratings of enjoyment, mood, and arousal as the patients listened to preferred pleasant music and unpreferred music by using Visual Analogue Scales (SI Methods). The preferred and unpreferred status of the music was operationalized along a continuum of pleasantness [ 4 ].

The problem with (4) is that the reader has to switch from the point of view of the experiment (passive voice) to the point of view of the experimenter (active voice). This switch causes confusion about the performer of the actions in the first and the third sentences. To improve the coherence and fluency of the paragraph above, you should be consistent in choosing the point of view: first person “we” or passive voice [ 5 ]. Let’s consider two revised examples in (5).

5a. We programmed behavioral computer-based experiments of Study 1 by using E-Prime. We took ratings of enjoyment, mood, and arousal by using Visual Analogue Scales (SI Methods) as the patients listened to preferred pleasant music and unpreferred music. We operationalized the preferred and unpreferred status of the music along a continuum of pleasantness. 5b. Behavioral computer-based experiments of Study 1 were programmed by using E-Prime. Ratings of enjoyment, mood, and arousal were taken as the patients listened to preferred pleasant music and unpreferred music by using Visual Analogue Scales (SI Methods). The preferred and unpreferred status of the music was operationalized along a continuum of pleasantness.

If you choose the point of view of the experimenter, then you may end up with repetitive “we did this” sentences. For many readers, paragraphs with sentences all beginning with “we” may also sound disruptive. So if you choose active sentences, you need to keep the number of “we” subjects to a minimum and vary the beginnings of the sentences [ 6 ].

Interestingly, recent studies have reported that the Materials and Methods section is the only section in research papers in which passive voice predominantly overrides the use of the active voice [ 5 , 7 , 8 , 9 ]. For example, Martínez shows a significant drop in active voice use in the Methods sections based on the corpus of 1 million words of experimental full text research articles in the biological sciences [ 7 ]. According to the author, the active voice patterned with “we” is used only as a tool to reveal personal responsibility for the procedural decisions in designing and performing experimental work. This means that while all other sections of the research paper use active voice, passive voice is still the most predominant in Materials and Methods sections.

Writing Materials and Methods sections is a meticulous and time consuming task requiring extreme accuracy and clarity. This is why when you complete your draft, you should ask for as much feedback from your colleagues as possible. Numerous readers of this section will help you identify the missing links and improve the technical style of this section.

Rule 3: Be meticulous and accurate in describing the Materials and Methods. Do not change the point of view within one paragraph.

3.2. writing results section.

For many authors, writing the Results section is more intimidating than writing the Materials and Methods section . If people are interested in your paper, they are interested in your results. That is why it is vital to use all your writing skills to objectively present your key findings in an orderly and logical sequence using illustrative materials and text.

Your Results should be organized into different segments or subsections where each one presents the purpose of the experiment, your experimental approach, data including text and visuals (tables, figures, schematics, algorithms, and formulas), and data commentary. For most journals, your data commentary will include a meaningful summary of the data presented in the visuals and an explanation of the most significant findings. This data presentation should not repeat the data in the visuals, but rather highlight the most important points. In the “standard” research paper approach, your Results section should exclude data interpretation, leaving it for the Discussion section. However, interpretations gradually and secretly creep into research papers: “Reducing the data, generalizing from the data, and highlighting scientific cases are all highly interpretive processes. It should be clear by now that we do not let the data speak for themselves in research reports; in summarizing our results, we interpret them for the reader” [ 10 ]. As a result, many journals including the Journal of Experimental Medicine and the Journal of Clinical Investigation use joint Results/Discussion sections, where results are immediately followed by interpretations.

Another important aspect of this section is to create a comprehensive and supported argument or a well-researched case. This means that you should be selective in presenting data and choose only those experimental details that are essential for your reader to understand your findings. You might have conducted an experiment 20 times and collected numerous records, but this does not mean that you should present all those records in your paper. You need to distinguish your results from your data and be able to discard excessive experimental details that could distract and confuse the reader. However, creating a picture or an argument should not be confused with data manipulation or falsification, which is a willful distortion of data and results. If some of your findings contradict your ideas, you have to mention this and find a plausible explanation for the contradiction.

In addition, your text should not include irrelevant and peripheral information, including overview sentences, as in (6).

6. To show our results, we first introduce all components of experimental system and then describe the outcome of infections.

Indeed, wordiness convolutes your sentences and conceals your ideas from readers. One common source of wordiness is unnecessary intensifiers. Adverbial intensifiers such as “clearly,” “essential,” “quite,” “basically,” “rather,” “fairly,” “really,” and “virtually” not only add verbosity to your sentences, but also lower your results’ credibility. They appeal to the reader’s emotions but lower objectivity, as in the common examples in (7):

7a. Table 3 clearly shows that … 7b. It is obvious from figure 4 that …

Another source of wordiness is nominalizations, i.e., nouns derived from verbs and adjectives paired with weak verbs including “be,” “have,” “do,” “make,” “cause,” “provide,” and “get” and constructions such as “there is/are.”

8a. We tested the hypothesis that there is a disruption of membrane asymmetry. 8b. In this paper we provide an argument that stem cells repopulate injured organs.

In the sentences above, the abstract nominalizations “disruption” and “argument” do not contribute to the clarity of the sentences, but rather clutter them with useless vocabulary that distracts from the meaning. To improve your sentences, avoid unnecessary nominalizations and change passive verbs and constructions into active and direct sentences.

9a. We tested the hypothesis that the membrane asymmetry is disrupted. 9b. In this paper we argue that stem cells repopulate injured organs.

Your Results section is the heart of your paper, representing a year or more of your daily research. So lead your reader through your story by writing direct, concise, and clear sentences.

Rule 4: Be clear, concise, and objective in describing your Results.

3.3. now it is time for your introduction.

Now that you are almost half through drafting your research paper, it is time to update your outline. While describing your Methods and Results, many of you diverged from the original outline and re-focused your ideas. So before you move on to create your Introduction, re-read your Methods and Results sections and change your outline to match your research focus. The updated outline will help you review the general picture of your paper, the topic, the main idea, and the purpose, which are all important for writing your introduction.

The best way to structure your introduction is to follow the three-move approach shown in Table 3 .

Adapted from Swales and Feak [ 11 ].

The moves and information from your outline can help to create your Introduction efficiently and without missing steps. These moves are traffic signs that lead the reader through the road of your ideas. Each move plays an important role in your paper and should be presented with deep thought and care. When you establish the territory, you place your research in context and highlight the importance of your research topic. By finding the niche, you outline the scope of your research problem and enter the scientific dialogue. The final move, “occupying the niche,” is where you explain your research in a nutshell and highlight your paper’s significance. The three moves allow your readers to evaluate their interest in your paper and play a significant role in the paper review process, determining your paper reviewers.

Some academic writers assume that the reader “should follow the paper” to find the answers about your methodology and your findings. As a result, many novice writers do not present their experimental approach and the major findings, wrongly believing that the reader will locate the necessary information later while reading the subsequent sections [ 5 ]. However, this “suspense” approach is not appropriate for scientific writing. To interest the reader, scientific authors should be direct and straightforward and present informative one-sentence summaries of the results and the approach.

Another problem is that writers understate the significance of the Introduction. Many new researchers mistakenly think that all their readers understand the importance of the research question and omit this part. However, this assumption is faulty because the purpose of the section is not to evaluate the importance of the research question in general. The goal is to present the importance of your research contribution and your findings. Therefore, you should be explicit and clear in describing the benefit of the paper.

The Introduction should not be long. Indeed, for most journals, this is a very brief section of about 250 to 600 words, but it might be the most difficult section due to its importance.

Rule 5: Interest your reader in the Introduction section by signalling all its elements and stating the novelty of the work.

3.4. discussion of the results.

For many scientists, writing a Discussion section is as scary as starting a paper. Most of the fear comes from the variation in the section. Since every paper has its unique results and findings, the Discussion section differs in its length, shape, and structure. However, some general principles of writing this section still exist. Knowing these rules, or “moves,” can change your attitude about this section and help you create a comprehensive interpretation of your results.

The purpose of the Discussion section is to place your findings in the research context and “to explain the meaning of the findings and why they are important, without appearing arrogant, condescending, or patronizing” [ 11 ]. The structure of the first two moves is almost a mirror reflection of the one in the Introduction. In the Introduction, you zoom in from general to specific and from the background to your research question; in the Discussion section, you zoom out from the summary of your findings to the research context, as shown in Table 4 .

Adapted from Swales and Feak and Hess [ 11 , 12 ].

The biggest challenge for many writers is the opening paragraph of the Discussion section. Following the moves in Table 1 , the best choice is to start with the study’s major findings that provide the answer to the research question in your Introduction. The most common starting phrases are “Our findings demonstrate . . .,” or “In this study, we have shown that . . .,” or “Our results suggest . . .” In some cases, however, reminding the reader about the research question or even providing a brief context and then stating the answer would make more sense. This is important in those cases where the researcher presents a number of findings or where more than one research question was presented. Your summary of the study’s major findings should be followed by your presentation of the importance of these findings. One of the most frequent mistakes of the novice writer is to assume the importance of his findings. Even if the importance is clear to you, it may not be obvious to your reader. Digesting the findings and their importance to your reader is as crucial as stating your research question.

Another useful strategy is to be proactive in the first move by predicting and commenting on the alternative explanations of the results. Addressing potential doubts will save you from painful comments about the wrong interpretation of your results and will present you as a thoughtful and considerate researcher. Moreover, the evaluation of the alternative explanations might help you create a logical step to the next move of the discussion section: the research context.

The goal of the research context move is to show how your findings fit into the general picture of the current research and how you contribute to the existing knowledge on the topic. This is also the place to discuss any discrepancies and unexpected findings that may otherwise distort the general picture of your paper. Moreover, outlining the scope of your research by showing the limitations, weaknesses, and assumptions is essential and adds modesty to your image as a scientist. However, make sure that you do not end your paper with the problems that override your findings. Try to suggest feasible explanations and solutions.

If your submission does not require a separate Conclusion section, then adding another paragraph about the “take-home message” is a must. This should be a general statement reiterating your answer to the research question and adding its scientific implications, practical application, or advice.

Just as in all other sections of your paper, the clear and precise language and concise comprehensive sentences are vital. However, in addition to that, your writing should convey confidence and authority. The easiest way to illustrate your tone is to use the active voice and the first person pronouns. Accompanied by clarity and succinctness, these tools are the best to convince your readers of your point and your ideas.

Rule 6: Present the principles, relationships, and generalizations in a concise and convincing tone.

4. choosing the best working revision strategies.

Now that you have created the first draft, your attitude toward your writing should have improved. Moreover, you should feel more confident that you are able to accomplish your project and submit your paper within a reasonable timeframe. You also have worked out your writing schedule and followed it precisely. Do not stop ― you are only at the midpoint from your destination. Just as the best and most precious diamond is no more than an unattractive stone recognized only by trained professionals, your ideas and your results may go unnoticed if they are not polished and brushed. Despite your attempts to present your ideas in a logical and comprehensive way, first drafts are frequently a mess. Use the advice of Paul Silvia: “Your first drafts should sound like they were hastily translated from Icelandic by a non-native speaker” [ 2 ]. The degree of your success will depend on how you are able to revise and edit your paper.

The revision can be done at the macrostructure and the microstructure levels [ 13 ]. The macrostructure revision includes the revision of the organization, content, and flow. The microstructure level includes individual words, sentence structure, grammar, punctuation, and spelling.

The best way to approach the macrostructure revision is through the outline of the ideas in your paper. The last time you updated your outline was before writing the Introduction and the Discussion. Now that you have the beginning and the conclusion, you can take a bird’s-eye view of the whole paper. The outline will allow you to see if the ideas of your paper are coherently structured, if your results are logically built, and if the discussion is linked to the research question in the Introduction. You will be able to see if something is missing in any of the sections or if you need to rearrange your information to make your point.

The next step is to revise each of the sections starting from the beginning. Ideally, you should limit yourself to working on small sections of about five pages at a time [ 14 ]. After these short sections, your eyes get used to your writing and your efficiency in spotting problems decreases. When reading for content and organization, you should control your urge to edit your paper for sentence structure and grammar and focus only on the flow of your ideas and logic of your presentation. Experienced researchers tend to make almost three times the number of changes to meaning than novice writers [ 15 , 16 ]. Revising is a difficult but useful skill, which academic writers obtain with years of practice.

In contrast to the macrostructure revision, which is a linear process and is done usually through a detailed outline and by sections, microstructure revision is a non-linear process. While the goal of the macrostructure revision is to analyze your ideas and their logic, the goal of the microstructure editing is to scrutinize the form of your ideas: your paragraphs, sentences, and words. You do not need and are not recommended to follow the order of the paper to perform this type of revision. You can start from the end or from different sections. You can even revise by reading sentences backward, sentence by sentence and word by word.

One of the microstructure revision strategies frequently used during writing center consultations is to read the paper aloud [ 17 ]. You may read aloud to yourself, to a tape recorder, or to a colleague or friend. When reading and listening to your paper, you are more likely to notice the places where the fluency is disrupted and where you stumble because of a very long and unclear sentence or a wrong connector.

Another revision strategy is to learn your common errors and to do a targeted search for them [ 13 ]. All writers have a set of problems that are specific to them, i.e., their writing idiosyncrasies. Remembering these problems is as important for an academic writer as remembering your friends’ birthdays. Create a list of these idiosyncrasies and run a search for these problems using your word processor. If your problem is demonstrative pronouns without summary words, then search for “this/these/those” in your text and check if you used the word appropriately. If you have a problem with intensifiers, then search for “really” or “very” and delete them from the text. The same targeted search can be done to eliminate wordiness. Searching for “there is/are” or “and” can help you avoid the bulky sentences.

The final strategy is working with a hard copy and a pencil. Print a double space copy with font size 14 and re-read your paper in several steps. Try reading your paper line by line with the rest of the text covered with a piece of paper. When you are forced to see only a small portion of your writing, you are less likely to get distracted and are more likely to notice problems. You will end up spotting more unnecessary words, wrongly worded phrases, or unparallel constructions.

After you apply all these strategies, you are ready to share your writing with your friends, colleagues, and a writing advisor in the writing center. Get as much feedback as you can, especially from non-specialists in your field. Patiently listen to what others say to you ― you are not expected to defend your writing or explain what you wanted to say. You may decide what you want to change and how after you receive the feedback and sort it in your head. Even though some researchers make the revision an endless process and can hardly stop after a 14th draft; having from five to seven drafts of your paper is a norm in the sciences. If you can’t stop revising, then set a deadline for yourself and stick to it. Deadlines always help.

Rule 7: Revise your paper at the macrostructure and the microstructure level using different strategies and techniques. Receive feedback and revise again.

5. it is time to submit.

It is late at night again. You are still in your lab finishing revisions and getting ready to submit your paper. You feel happy ― you have finally finished a year’s worth of work. You will submit your paper tomorrow, and regardless of the outcome, you know that you can do it. If one journal does not take your paper, you will take advantage of the feedback and resubmit again. You will have a publication, and this is the most important achievement.

What is even more important is that you have your scheduled writing time that you are going to keep for your future publications, for reading and taking notes, for writing grants, and for reviewing papers. You are not going to lose stamina this time, and you will become a productive scientist. But for now, let’s celebrate the end of the paper.

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Expert Commentary

Research strategy guide for finding quality, credible sources

Strategies for finding academic studies and other information you need to give your stories authority and depth

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This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License .

by Keely Wilczek, The Journalist's Resource May 20, 2011

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Knowing how to conduct deeper research efficiently and effectively is a critical skill for journalists — especially in the information age. It is, like other facets of the profession such as interviewing, a matter of practice and establishing good habits. And once you find a successful routine for information-gathering, it will pay dividends time and again.

Journalists need to be able to do many kinds of research. This article focuses on creating a research strategy that will help you find academic studies and related scholarly information. These sources can, among other things, give your stories extra authority and depth — and thereby distinguish your work. You can see examples of such studies — and find many relevant ones for your stories — by searching the Journalist’s Resource database . But that is just a representative sample of what exists in the research world.

The first step is to create a plan for seeking the information you need. This requires you to take time initially and to proceed with care, but it will ultimately pay off in better results. The research strategy covered in this article involves the following steps:

Get organized

Articulate your topic, locate background information.

• Identify your information needs

List keywords and concepts for search engines and databases

Consider the scope of your topic, conduct your searches, evaluate the information sources you found, analyze and adjust your research strategy.

Being organized is an essential part of effective research strategy. You should create a record of your strategy and your searches. This will prevent you from repeating searches in the same resources and from continuing to use ineffective terms. It will also help you assess the success or failure of your research strategy as you go through the process. You also may want to consider tracking and organizing citations and links in bibliographic software such as Zotero . (See this helpful resource guide about using Zotero.)

Next, write out your topic in a clear and concise manner. Good research starts with a specific focus.

For example, let’s say you are writing a story about the long-range health effects of the explosion at the Chernobyl Nuclear Power Plant based on a study published in Environmental Health Perspectives titled, “The Chernobyl Accident 20 Years On: An Assessment of the Health Consequences and the International Response.” (The study is summarized in Journalist’s Resource here .)

A statement of your topic might be, “Twenty years after the Chernobyl disaster, scientists are still learning the affects of the accident on the health of those who lived in the surrounding area and their descendants.”

If you have a good understanding of the Chernobyl disaster, proceed to the next step, “Identify the information you need.” If not, it’s time to gather background information. This will supply you with the whos and the whens of the topic. It will also provide you with a broader context as well as the important terminology.

Excellent sources of background information are subject-specific encyclopedias and dictionaries, books, and scholarly articles, and organizations’ websites. You should always consult more than one source so you can compare for accuracy and bias.

For your story about Chernobyl, you might want to consult some of the following sources:

• Frequently Asked Chernobyl Questions , International Atomic Agency
• Chernobyl Accident 1986 , World Nuclear Association
• Chernobyl: Consequences of the Catastrophe for People and the Environment , New York Academy of Sciences, 2009.
• “Chernobyl Disaster,” Encyclopedia Britannica, last updated 2013.

Identify the information you need

What information do you need to write your story? One way to determine this is to turn your overall topic into a list of questions to be answered. This will help you identify the type and level of information you need. Some possible questions on consequences of the Chernobyl accident are:

• What are the proven health effects?
• What are some theorized health effects?
• Is there controversy about any of these studies?
• What geographic area is being studied?
• What are the demographic characteristics of the population being studied?
• Was there anything that could have been done at the time to mitigate these effects?

Looking at these questions, it appears that scientific studies and scholarly articles about those studies, demographic data, disaster response analysis, and government documents and publications from the Soviet Union and Ukraine would be needed.

Now you need to determine what words you will use to enter in the search boxes within resources. One way to begin is to extract the most important words and phrases from the questions produced in the previous step. Next, think about alternative words and phrases that you might use. Always keep in mind that different people may write or talk about the same topic in different ways. Important concepts can referred to differently or be spelled differently depending on country of origin or field of study.

For the Chernobyl health story, some search keyword options are: “Chernobyl,” “Chornobyl”; “disaster,” “catastrophe,” “explosion”; “health,” “disease,” “illness,” “medical conditions”; “genetic mutation,” “gene mutation,” “germ-line mutation,” “hereditary disease.” Used in different combinations, these can unearth a wide variety of resources.

Next you should identify the scope of your topic and any limitations it puts on your searches. Some examples of limitations are language, publication date, and publication type. Every database and search engine will have its own rules so you may need to click on an advanced search option in order to input these limitations.

It is finally time to start looking for information but identifying which resources to use is not always easy to do. First, if you are part of an organization, find out what, if any, resources you have access to through a subscription. Examples of subscription resources are LexisNexis and JSTOR. If your organization does not provide subscription resources, find out if you can get access to these sources through your local library. Should you not have access to any subscription resources appropriate for your topic, look at some of the many useful free resources on the internet.

Here are some examples of sources for free information:

• PLoS , Public Library of Science
• Google Scholar
• SSRN , Social Science Research Network
• FDsys , U.S. Government documents and publications
• World Development Indicators , World Bank
• Pubmed , service of the U.S. National Library of Medicine

More quality sites, and search tips, are here among the other research articles at Journalist’s Resource.

As you only want information from the most reliable and suitable sources, you should always evaluate your results. In doing this, you can apply journalism’s Five W’s (and One H):

• Who : Who is the author and what are his/her credentials in this topic?
• What: Is the material primary or secondary in nature?
• Where: Is the publisher or organization behind the source considered reputable? Does the website appear legitimate?
• When: Is the source current or does it cover the right time period for your topic?
• Why: Is the opinion or bias of the author apparent and can it be taken into account?
• How: Is the source written at the right level for your needs? Is the research well-documented?

Were you able to locate the information you needed? If not, now it is time to analyze why that happened. Perhaps there are better resources or different keywords and concepts you could have tried. Additional background information might supply you with other terminology to use. It is also possible that the information you need is just not available in the way you need it and it may be necessary to consult others for assistance like an expert in the topic or a professional librarian.

Keely Wilczek is a research librarian at the Harvard Kennedy School. Tags: training

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6 Research strategy

A research strategy introduces the main components of a research project such as the research topic area and focus, the research perspective (see Sections 1 and 2), the research design, and the research methods (these are discussed below). It refers to how you propose to answer the research questions set and how you will implement the methodology.

In the first part of this course, you started to identify your research topic, to develop your research statement and you thought about possible research question(s). While you might already have clear research questions or objectives, it is possible that, at this stage, you are uncertain about the most appropriate strategy to implement in order to address those questions. This section looks briefly at a few research strategies you are likely to adopt.

Figure 5 shows the four main types of research strategy: case study, qualitative interviews, quantitative survey and action-oriented research. It is likely that you will use one of the first three; you are less likely to use action-oriented research.

Here is what each of these strategies entails:

• Case Study : This focuses on an in-depth investigation of a single case (e.g. one organisation) or a small number of cases. In case study research generally, information is sought from different sources and through the use of different types of data such as observations, survey, interviews and analysis of documents. Data can be qualitative, quantitative or a mix of both. Case study research allows a composite and multifaceted investigation of the issue or problem.
• Qualitative interviews : There are different types of qualitative interviews (e.g. structured, semi-structured, unstructured) and this is the most widely used method for gathering data. Interviews allow access to rich information. They require extensive planning concerning the development of the structure, decisions about who to interview and how, whether to conduct individual or group interviews, and how to record and analyse them. Interviewees need a wide range of skills, including good social skills, listening skills and communication skills. Interviews are also time-consuming to conduct and they are prone to problems and biases that need to be minimised during the design stage.
• Quantitative survey : This is a widely used method in business research and allows access to significantly high numbers of participants. The availability of online sites enables the wide and cheap distribution of surveys and the organisation of the responses. Although the development of questions may appear easy, to develop a meaningful questionnaire that allows the answering of research questions is difficult. Questionnaires need to appeal to respondents, cannot be too long, too intrusive or too difficult to understand. They also need to measure accurately the issue under investigation. For these reasons it is also advisable, when possible, to use questionnaires that are available on the market and have already been thoroughly validated. This is highly recommended for projects such as the one you need to carry out for this course. When using questionnaires decisions have to be made about the size of the sample and whether and when this is representative of the whole population studied. Surveys can be administered to the whole population (census), for example to all employees of a specific organisation.
• Action-oriented research : This refers to practical business research which is directed towards a change or the production of recommendations for change. Action-oriented research is a participatory process which brings together theory and practice, action and reflection. The project is often carried out by insiders. This is because it is grounded in the need to actively involve participants in order for them to develop ownership of the project. After the project, participants will have to implement the change.

Action-oriented research is not exactly action research, even though they are both grounded in the same assumptions (e.g. to produce change). Action research is a highly complex approach to research, reflection and change which is not always achievable in practice (Cameron and Price, 2009). Furthermore action researchers have to be highly skilled and it is unlikely that for this specific project you will be involved in action research. For these reasons this overview focuses on the less pure action-oriented research strategy. If you are interested in exploring this strategy and action research further, you might want to read Chapter 14 of Cameron and Price (2009).

It is possible for you to choose a strategy that includes the use of secondary data. Secondary data is data that has been collected by other people (e.g. employee surveys, market research data, census). Using secondary data for your research project needs to be justified in that it meets the requirements of the research questions. The use of secondary data has obvious benefits in terms of saving money and time. However, it is important to ascertain the quality of the data and how it was collected; for example, data collected by government agencies would be good quality but it may not necessary meet the needs of your project.

It is important to note that there should be consistency between the perspective (subjective or objective) and the methodology employed. This means that the type of strategy adopted needs to be coherent and that its various elements need to fit in with each other, whether the research is grounded on primary or secondary data.

Now watch this video clip in which Dr Rebecca Hewett, Prof Mark Saunders, Prof Gillian Symon and Prof David Guest discuss the importance of setting the right research question, what strategy they adopted to come up with specific research questions for their projects, and how they refined these initial research questions to focus their research.

Make notes on how you might apply some of these strategies to develop your own research question.

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Educational resources and simple solutions for your research journey

Reading Research Papers: Strategies to do it Effectively

Table of Contents

Reading research literature can be challenging for students or early career researchers

In academia, you are often expected to read research papers quickly and carefully, either as part of your research or to review them. In fact, a study finds that researchers are expected to spend 23% of their total work time reading research publications. 1  In 2012, scientists in the US read, on average, 22 scholarly articles per month (or 264 per year). 2

The academic language used in research papers is concise, precise, and authoritative, and a readers’ familiarity with the scientific field often shapes their perceptions and understanding of the literature. Researchers, especially students, may need help learning academic vocabulary and processing academic language in their journey toward becoming independent learners of science. A study on research reading habits reported that the vast majority of students are researchers engaging with primary literature from at least one source. 3  While intrinsically motivated senior researchers quickly read multiple research papers per day, sophomores rarely engage in reading scientific journals, probably due to the difficulty in understanding. And it’s not just students or early career researchers; even experienced researchers exploring interdisciplinary literature may find reading and understanding unfamiliar scientific language challenging. A lack of prior knowledge, and possibly low self-confidence, highlights a need for intrinsic and extrinsic motivation to develop research paper reading habits and strategies to efficiently process scientific literature.

Researchers at different career stages read papers for various purposes

Students and researchers agree that knowing how to read a research paper effectively is important for scientific development. However, how they engage with scientific literature and their reading strategies differ depending on their position. The transition from learner to proficient reader spans multiple career stages over an extended period. Undergraduates read scientific works as part of their assignment or to broaden their knowledge and prioritize reading the abstract and discussion sections. They are more likely to take author statements in the discussion section at face value than interpret primary results. This is because they often lack critical reading abilities, undervaluing the importance of the results section or critical data interpretation. In contrast, experienced researchers are more confident in their reading abilities. Their research paper reading strategy involves understanding the research methods and critically evaluating the data. On the other hand, undergraduates and early career researchers find the method and result sections the most challenging. PhD students and Postdoctoral researchers’ engagement with the scientific text lies between these two groups. Thus, students’ research paper reading strategy is usually a narrative-centric approach, drawing their understanding from the author’s descriptions. In contrast, experienced researchers employ data-centric strategies, critically evaluating the data provided. 4

Strategies for reading academic articles

  Once a student learns how to read scientific papers effectively, they can grow the spirit of scientific inquiry. For this, they need to read critically, evaluating the meaning, aim, and content of a work. There are strategies to help inexperienced readers develop the critical reading and thinking skills required for efficiently reading research papers.

One great approach is the Watson Glaser RED model of critical thinking based on  R ecognizing assumptions,  E valuating arguments, and  D rawing inferences, interpretations, and deductions. 5  Another example is the CREATE method, which structures reading as  C onsider,  R ead,  E lucidate the hypothesis,  A nalyze and interpret data, and  T hink of the next  E xperiment. 3  This method adopts a reading strategy used by experienced researchers to identify gaps in the existing literature and develop new ideas or concepts for future research.

Selective approach to scientific reading: How to read a research paper effectively

The development of reading skills is influenced by a change from extrinsic to intrinsic motivation for reading, which, in turn, is influenced by the increased familiarity with scientific work, terminology, and prior knowledge. The following tips can help students and early career researchers hone research paper reading skills exhibited by more experienced researchers.

• Find something that encourages or holds your interests, even if it bothers or confuses you, and use this to drive your analysis.
• Underline words, sentences, and passages that address your specific purpose.
• Use specific sections to determine if the entire paper is worth reading.
• Start with the abstract, then move on to figures, introduction, and discussions. Focusing on these key sections can help you read a research paper quickly.
• A good research paper reading strategy involves reading the text several times, taking notes, asking questions, and underlining key information.
• Respond critically 6  using a research paper reading strategy, including applying your personal experiences and existing knowledge to the reading process.
• Interpret and reflect upon what you read by writing about it and discussing it with others. Research articles are always open to questioning and are differently interpreted based on researcher ideology.
• A good research paper reading habit involves seeking out other texts and sources, which would give the research question a global perspective, and people who can help you in your research and learning.

Those were some tips for reading academic articles in an efficient manner. Following these steps should guide you in developing a systemic strategy for reading research papers and help you progress in your research career.

• Phillips, L. M., & Norris, S. P. Bridging the gap between the language of science and the language of school science through the use of adapted primary literature.  Res Sci Educ.  39 , 313-319 (2009)
• Van Noorden, R. Scientists may be reaching a peak in reading habits.  Nature , 15-17 (2014).
• Hubbard, K. E., & Dunbar, S. D. Perceptions of scientific research literature and strategies for reading papers depend on academic career stage.  PloS One 12 , e0189753 (2017).
• Hubbard, K. E., Dunbar, S. D., Peasland, E. L., Poon, J., & Solly, J. E. How do readers at different career stages approach reading a scientific research paper? A case study in the biological sciences.  Int J Sci Educ B , 1-17 (2022).
• Wulandari, R., & Hindrayani, A. Measuring Critical Thinking Skills with the RED Model. In  J Phys  1808 , 012030 (IOP Publishing, 2021).
• Van, L. H., Li, C. S., & Wan, R. Critical reading in higher education: A systematic review.  Think Skills Create.   44  (2022).

R Discovery is a literature search and research reading platform that accelerates your research discovery journey by keeping you updated on the latest, most relevant scholarly content. With 250M+ research articles sourced from trusted aggregators like CrossRef, Unpaywall, PubMed, PubMed Central, Open Alex and top publishing houses like Springer Nature, JAMA, IOP, Taylor & Francis, NEJM, BMJ, Karger, SAGE, Emerald Publishing and more, R Discovery puts a world of research at your fingertips.  

Try R Discovery Prime FREE for 1 week or upgrade at just US$72 a year to access premium features that let you listen to research on the go, read in your language, collaborate with peers, auto sync with reference managers, and much more. Choose a simpler, smarter way to find and read research – Download the app and start your free 7-day trial today !  

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How to Write a Research Proposal: (with Examples & Templates)

Table of Contents

Before conducting a study, a research proposal should be created that outlines researchers’ plans and methodology and is submitted to the concerned evaluating organization or person. Creating a research proposal is an important step to ensure that researchers are on track and are moving forward as intended. A research proposal can be defined as a detailed plan or blueprint for the proposed research that you intend to undertake. It provides readers with a snapshot of your project by describing what you will investigate, why it is needed, and how you will conduct the research.  

Your research proposal should aim to explain to the readers why your research is relevant and original, that you understand the context and current scenario in the field, have the appropriate resources to conduct the research, and that the research is feasible given the usual constraints.  

This article will describe in detail the purpose and typical structure of a research proposal , along with examples and templates to help you ace this step in your research journey.  

What is a Research Proposal ?  

A research proposal¹ ,²  can be defined as a formal report that describes your proposed research, its objectives, methodology, implications, and other important details. Research proposals are the framework of your research and are used to obtain approvals or grants to conduct the study from various committees or organizations. Consequently, research proposals should convince readers of your study’s credibility, accuracy, achievability, practicality, and reproducibility.   

With research proposals , researchers usually aim to persuade the readers, funding agencies, educational institutions, and supervisors to approve the proposal. To achieve this, the report should be well structured with the objectives written in clear, understandable language devoid of jargon. A well-organized research proposal conveys to the readers or evaluators that the writer has thought out the research plan meticulously and has the resources to ensure timely completion.  

Purpose of Research Proposals  

A research proposal is a sales pitch and therefore should be detailed enough to convince your readers, who could be supervisors, ethics committees, universities, etc., that what you’re proposing has merit and is feasible . Research proposals can help students discuss their dissertation with their faculty or fulfill course requirements and also help researchers obtain funding. A well-structured proposal instills confidence among readers about your ability to conduct and complete the study as proposed.  

Research proposals can be written for several reasons:³  

• To describe the importance of research in the specific topic  
• Address any potential challenges you may encounter  
• Showcase knowledge in the field and your ability to conduct a study  
• Apply for a role at a research institute  
• Convince a research supervisor or university that your research can satisfy the requirements of a degree program  
• Highlight the importance of your research to organizations that may sponsor your project  
• Identify implications of your project and how it can benefit the audience  

What Goes in a Research Proposal?    

Research proposals should aim to answer the three basic questions—what, why, and how.  

The What question should be answered by describing the specific subject being researched. It should typically include the objectives, the cohort details, and the location or setting.  

The Why question should be answered by describing the existing scenario of the subject, listing unanswered questions, identifying gaps in the existing research, and describing how your study can address these gaps, along with the implications and significance.  

The How question should be answered by describing the proposed research methodology, data analysis tools expected to be used, and other details to describe your proposed methodology.   

Research Proposal Example  

Here is a research proposal sample template (with examples) from the University of Rochester Medical Center. 4 The sections in all research proposals are essentially the same although different terminology and other specific sections may be used depending on the subject.  

Structure of a Research Proposal  

If you want to know how to make a research proposal impactful, include the following components:¹  

1. Introduction  

This section provides a background of the study, including the research topic, what is already known about it and the gaps, and the significance of the proposed research.  

2. Literature review  

This section contains descriptions of all the previous relevant studies pertaining to the research topic. Every study cited should be described in a few sentences, starting with the general studies to the more specific ones. This section builds on the understanding gained by readers in the Introduction section and supports it by citing relevant prior literature, indicating to readers that you have thoroughly researched your subject.  

3. Objectives  

Once the background and gaps in the research topic have been established, authors must now state the aims of the research clearly. Hypotheses should be mentioned here. This section further helps readers understand what your study’s specific goals are.  

4. Research design and methodology  

Here, authors should clearly describe the methods they intend to use to achieve their proposed objectives. Important components of this section include the population and sample size, data collection and analysis methods and duration, statistical analysis software, measures to avoid bias (randomization, blinding), etc.  

5. Ethical considerations  

This refers to the protection of participants’ rights, such as the right to privacy, right to confidentiality, etc. Researchers need to obtain informed consent and institutional review approval by the required authorities and mention this clearly for transparency.  

6. Budget/funding  

Researchers should prepare their budget and include all expected expenditures. An additional allowance for contingencies such as delays should also be factored in.  

7. Appendices  

This section typically includes information that supports the research proposal and may include informed consent forms, questionnaires, participant information, measurement tools, etc.  

8. Citations  

Important Tips for Writing a Research Proposal  

Writing a research proposal begins much before the actual task of writing. Planning the research proposal structure and content is an important stage, which if done efficiently, can help you seamlessly transition into the writing stage. 3,5  

The Planning Stage  

• Manage your time efficiently. Plan to have the draft version ready at least two weeks before your deadline and the final version at least two to three days before the deadline.
• What is the primary objective of your research?  
• Will your research address any existing gap?  
• What is the impact of your proposed research?  
• Do people outside your field find your research applicable in other areas?  
• If your research is unsuccessful, would there still be other useful research outcomes?  

  The Writing Stage  

• Create an outline with main section headings that are typically used.  
• Focus only on writing and getting your points across without worrying about the format of the research proposal , grammar, punctuation, etc. These can be fixed during the subsequent passes. Add details to each section heading you created in the beginning.   
• Ensure your sentences are concise and use plain language. A research proposal usually contains about 2,000 to 4,000 words or four to seven pages.  
• Don’t use too many technical terms and abbreviations assuming that the readers would know them. Define the abbreviations and technical terms.  
• Ensure that the entire content is readable. Avoid using long paragraphs because they affect the continuity in reading. Break them into shorter paragraphs and introduce some white space for readability.  
• Focus on only the major research issues and cite sources accordingly. Don’t include generic information or their sources in the literature review.  
• Proofread your final document to ensure there are no grammatical errors so readers can enjoy a seamless, uninterrupted read.  
• Use academic, scholarly language because it brings formality into a document.  
• Ensure that your title is created using the keywords in the document and is neither too long and specific nor too short and general.  
• Cite all sources appropriately to avoid plagiarism.  
• Make sure that you follow guidelines, if provided. This includes rules as simple as using a specific font or a hyphen or en dash between numerical ranges.  
• Ensure that you’ve answered all questions requested by the evaluating authority.  

Key Takeaways   

Here’s a summary of the main points about research proposals discussed in the previous sections:  

• A research proposal is a document that outlines the details of a proposed study and is created by researchers to submit to evaluators who could be research institutions, universities, faculty, etc.  
• Research proposals are usually about 2,000-4,000 words long, but this depends on the evaluating authority’s guidelines.  
• A good research proposal ensures that you’ve done your background research and assessed the feasibility of the research.  
• Research proposals have the following main sections—introduction, literature review, objectives, methodology, ethical considerations, and budget.  

Frequently Asked Questions  

Q1. How is a research proposal evaluated?  

A1. In general, most evaluators, including universities, broadly use the following criteria to evaluate research proposals . 6  

• Significance —Does the research address any important subject or issue, which may or may not be specific to the evaluator or university?  
• Content and design —Is the proposed methodology appropriate to answer the research question? Are the objectives clear and well aligned with the proposed methodology?  
• Sample size and selection —Is the target population or cohort size clearly mentioned? Is the sampling process used to select participants randomized, appropriate, and free of bias?  
• Timing —Are the proposed data collection dates mentioned clearly? Is the project feasible given the specified resources and timeline?  
• Data management and dissemination —Who will have access to the data? What is the plan for data analysis?  

Q2. What is the difference between the Introduction and Literature Review sections in a research proposal ?  

A2. The Introduction or Background section in a research proposal sets the context of the study by describing the current scenario of the subject and identifying the gaps and need for the research. A Literature Review, on the other hand, provides references to all prior relevant literature to help corroborate the gaps identified and the research need.  

Q3. How long should a research proposal be?  

A3. Research proposal lengths vary with the evaluating authority like universities or committees and also the subject. Here’s a table that lists the typical research proposal lengths for a few universities.  

Q4. What are the common mistakes to avoid in a research proposal ?  

A4. Here are a few common mistakes that you must avoid while writing a research proposal . 7  

• No clear objectives: Objectives should be clear, specific, and measurable for the easy understanding among readers.  
• Incomplete or unconvincing background research: Background research usually includes a review of the current scenario of the particular industry and also a review of the previous literature on the subject. This helps readers understand your reasons for undertaking this research because you identified gaps in the existing research.  
• Overlooking project feasibility: The project scope and estimates should be realistic considering the resources and time available.   
• Neglecting the impact and significance of the study: In a research proposal , readers and evaluators look for the implications or significance of your research and how it contributes to the existing research. This information should always be included.  
• Unstructured format of a research proposal : A well-structured document gives confidence to evaluators that you have read the guidelines carefully and are well organized in your approach, consequently affirming that you will be able to undertake the research as mentioned in your proposal.  
• Ineffective writing style: The language used should be formal and grammatically correct. If required, editors could be consulted, including AI-based tools such as Paperpal , to refine the research proposal structure and language.  

Thus, a research proposal is an essential document that can help you promote your research and secure funds and grants for conducting your research. Consequently, it should be well written in clear language and include all essential details to convince the evaluators of your ability to conduct the research as proposed.  

This article has described all the important components of a research proposal and has also provided tips to improve your writing style. We hope all these tips will help you write a well-structured research proposal to ensure receipt of grants or any other purpose.  

References  

• Sudheesh K, Duggappa DR, Nethra SS. How to write a research proposal? Indian J Anaesth. 2016;60(9):631-634. Accessed July 15, 2024. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5037942/  
• Writing research proposals. Harvard College Office of Undergraduate Research and Fellowships. Harvard University. Accessed July 14, 2024. https://uraf.harvard.edu/apply-opportunities/app-components/essays/research-proposals  
• What is a research proposal? Plus how to write one. Indeed website. Accessed July 17, 2024. https://www.indeed.com/career-advice/career-development/research-proposal  
• Research proposal template. University of Rochester Medical Center. Accessed July 16, 2024. https://www.urmc.rochester.edu/MediaLibraries/URMCMedia/pediatrics/research/documents/Research-proposal-Template.pdf  
• Tips for successful proposal writing. Johns Hopkins University. Accessed July 17, 2024. https://research.jhu.edu/wp-content/uploads/2018/09/Tips-for-Successful-Proposal-Writing.pdf  
• Formal review of research proposals. Cornell University. Accessed July 18, 2024. https://irp.dpb.cornell.edu/surveys/survey-assessment-review-group/research-proposals  
• 7 Mistakes you must avoid in your research proposal. Aveksana (via LinkedIn). Accessed July 17, 2024. https://www.linkedin.com/pulse/7-mistakes-you-must-avoid-your-research-proposal-aveksana-cmtwf/  

Paperpal is a comprehensive AI writing toolkit that helps students and researchers achieve 2x the writing in half the time. It leverages 21+ years of STM experience and insights from millions of research articles to provide in-depth academic writing, language editing, and submission readiness support to help you write better, faster.  

Get accurate academic translations, rewriting support, grammar checks, vocabulary suggestions, and generative AI assistance that delivers human precision at machine speed. Try for free or upgrade to Paperpal Prime starting at US$19 a month to access premium features, including consistency, plagiarism, and 30+ submission readiness checks to help you succeed.  

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This paper is in the following e-collection/theme issue:

Published on 16.8.2024 in Vol 26 (2024)

Human-Comparable Sensitivity of Large Language Models in Identifying Eligible Studies Through Title and Abstract Screening: 3-Layer Strategy Using GPT-3.5 and GPT-4 for Systematic Reviews

Authors of this article:

Original Paper

• Kentaro Matsui 1, 2 , MD, PhD   ; 
• Tomohiro Utsumi 2, 3 , MD   ; 
• Yumi Aoki 4 , PhD   ; 
• Taku Maruki 5 , MD   ; 
• Masahiro Takeshima 6 , MD, PhD   ; 
• Yoshikazu Takaesu 7 , MD, PhD  

1 Department of Clinical Laboratory, National Center Hospital, National Center of Neurology and Psychiatry, Kodaira, Japan

2 Department of Sleep-Wake Disorders, National Institute of Mental Health, National Center of Neurology and Psychiatry, Kodaira, Japan

3 Department of Psychiatry, The Jikei University School of Medicine, Tokyo, Japan

4 Graduate School of Nursing Science, St. Luke’s International University, Tokyo, Japan

5 Department of Neuropsychiatry, Kyorin University School of Medicine, Tokyo, Japan

6 Department of Neuropsychiatry, Akita University Graduate School of Medicine, Akita, Japan

7 Department of Neuropsychiatry, Graduate School of Medicine, University of the Ryukyus, Okinawa, Japan

Corresponding Author:

Yoshikazu Takaesu, MD, PhD

Department of Neuropsychiatry

Graduate School of Medicine

University of the Ryukyus

Okinawa, 903-0215

Phone: 81 98 895 3331

Email: [email protected]

Background: The screening process for systematic reviews is resource-intensive. Although previous machine learning solutions have reported reductions in workload, they risked excluding relevant papers.

Objective: We evaluated the performance of a 3-layer screening method using GPT-3.5 and GPT-4 to streamline the title and abstract-screening process for systematic reviews. Our goal is to develop a screening method that maximizes sensitivity for identifying relevant records.

Methods: We conducted screenings on 2 of our previous systematic reviews related to the treatment of bipolar disorder, with 1381 records from the first review and 3146 from the second. Screenings were conducted using GPT-3.5 (gpt-3.5-turbo-0125) and GPT-4 (gpt-4-0125-preview) across three layers: (1) research design, (2) target patients, and (3) interventions and controls. The 3-layer screening was conducted using prompts tailored to each study. During this process, information extraction according to each study’s inclusion criteria and optimization for screening were carried out using a GPT-4–based flow without manual adjustments. Records were evaluated at each layer, and those meeting the inclusion criteria at all layers were subsequently judged as included.

Results: On each layer, both GPT-3.5 and GPT-4 were able to process about 110 records per minute, and the total time required for screening the first and second studies was approximately 1 hour and 2 hours, respectively. In the first study, the sensitivities/specificities of the GPT-3.5 and GPT-4 were 0.900/0.709 and 0.806/0.996, respectively. Both screenings by GPT-3.5 and GPT-4 judged all 6 records used for the meta-analysis as included. In the second study, the sensitivities/specificities of the GPT-3.5 and GPT-4 were 0.958/0.116 and 0.875/0.855, respectively. The sensitivities for the relevant records align with those of human evaluators: 0.867-1.000 for the first study and 0.776-0.979 for the second study. Both screenings by GPT-3.5 and GPT-4 judged all 9 records used for the meta-analysis as included. After accounting for justifiably excluded records by GPT-4, the sensitivities/specificities of the GPT-4 screening were 0.962/0.996 in the first study and 0.943/0.855 in the second study. Further investigation indicated that the cases incorrectly excluded by GPT-3.5 were due to a lack of domain knowledge, while the cases incorrectly excluded by GPT-4 were due to misinterpretations of the inclusion criteria.

Conclusions: Our 3-layer screening method with GPT-4 demonstrated acceptable level of sensitivity and specificity that supports its practical application in systematic review screenings. Future research should aim to generalize this approach and explore its effectiveness in diverse settings, both medical and nonmedical, to fully establish its use and operational feasibility.

Introduction

Large language models (LLMs) with extensive parameters, honed on substantial textual data, have seen striking advancements recently. Following OpenAI’s third-generation Generative Pre-trained Transformer (GPT-3), LLMs now possess advanced competencies in various natural language processing tasks [ 1 ]. Among these, ChatGPT, which is built on GPT-3.5—an iteration that improves upon GPT-3 by integrating both supervised and reinforcement learning techniques—has received particular attention [ 2 , 3 ]. GPT-3.5 has shown exceptional performance in the medical domain, achieving remarkable results on medical licensing examinations across different regions [ 4 ]. Furthermore, GPT-4, the successor to GPT-3.5, has exhibited superior performance [ 5 ], with its contextual understanding abilities potentially exceeding those of humans [ 6 , 7 ]. Beyond its use for language editing [ 8 , 9 ], both GPT-3.5 and GPT-4 have proven to be effective tools for analyzing and comprehending the abstracts of research papers, offering potential benefits in the screening process for systematic reviews.

Systematic reviews and subsequent meta-analyses bear crucial clinical significance. The screening of titles and abstracts is a crucial step in this process [ 10 - 13 ], often involving more than 1000 papers identified via targeted keyword searches [ 14 ]. This screening process can take approximately 1 hour for every 60-120 papers [ 10 ], which is a substantial drain on human and time resources. In addition, human error is inevitable in the screening process [ 15 - 17 ], and the number of such errors can increase as the amount of paper to be screened increases possibly due to fatigue and cognitive overload [ 18 , 19 ]. To mitigate this labor-intensive task, attempts have been made to use text mining and machine learning technologies [ 17 , 20 - 29 ]. Although these methods have successfully reduced the workload, they risk omitting relevant papers, which could result in a high false-negative rate. Specifically, several studies reported the exclusion of records that should have been included in the meta-analysis [ 20 , 21 , 23 , 29 ]. Consequently, using machine learning techniques, such as natural language processing, to assist with abstract screening has not yet become widely adopted [ 14 , 30 ]. For systematic reviews, maintaining high sensitivity for studies eligible for full-text assessment, ideally at 100% [ 10 ], is crucial if they are to be fully supplanted by an automated process.

With the advanced language-processing capabilities of GPT-3.5 and GPT-4 [ 2 , 5 ], there has been an expectation of achieving higher accuracy in screening processes. Kohandel Gargari et al [ 31 ] conducted title and abstract screening using GPT-3.5, but the sensitivity for identifying relevant papers remained at a maximum of 69%, even after attempting various prompt modifications. Khraisha et al [ 32 ] explored the use of GPT-4 across different systematic review processes and found that the sensitivity for title and abstract screening ranged between 42% and 50%. Guo et al [ 33 ] have also demonstrated the use of GPT-4 in title and abstract screenings; however, the sensitivity for relevant papers was limited to 76%, highlighting the challenge of unintentionally excluding necessary records. Notably, Tran et al [ 34 ] used GPT-3.5 for title and abstract screening with rigorous prompt adjustments, achieving a high sensitivity of 97.1% for relevant papers. While this high-sensitivity level might already be suitable for practical use in the systematic review process, its specificity was limited to 37.7% [ 34 ].

The aim of this study is to develop a title- and abstract-screening method using GPT-3.5 and GPT-4 that achieves as high a sensitivity as possible. Although the method of using GPT-3.5 by Tran et al [ 34 ] achieved high sensitivity for identifying relevant papers, we aim to maintain high sensitivity while also improving specificity through a unique approach that incorporates GPT-4. To achieve this, we subdivided the process of determining inclusion for systematic reviews [ 11 ] involving 3 layers of screening. By breaking down the screening process into multiple steps, each addressing a specific aspect, we aimed to optimize the performance of the language models. In this study, we regarded the results of human screening as the gold standard and calculated the sensitivity and specificity of the GPT-3.5 and GPT-4 screening results in comparison with them. Furthermore, we carefully examined the records that were erroneously excluded by GPT-3.5/GPT-4. This examination was conducted to assess the appropriateness of their exclusion.

Language Model Details

GPT-3.5 and GPT-4, LLMs used in this study, are accessible through ChatGPT. However, ChatGPT does not support processing multiple queries against the titles and abstracts of scholarly papers simultaneously. To address this limitation, we leveraged the application programming interfaces (APIs) of GPT-3.5 and GPT-4, known as gpt-3.5-turbo and gpt-4-turbo-preview, respectively [ 35 ].

For gpt-3.5-turbo, we used the most current model available, gpt-3.5-turbo-0125. This model could be used at a low cost of US $0.50 per 1M tokens for input and US $1.50 per 1M tokens for output, with approximately 750 tokens corresponding to 1000 words [ 36 ]. Similarly, for GPT-4, we used the latest model available, gpt-4-0125-preview, which was available at a cost of US $10.00 per 1M tokens for input and US $30.00 per 1M tokens for output [ 36 ].

Calling the GPT-3.5 and GPT-4 API

In this study, we used Google Spreadsheet and Google Apps Script to interface with the GPT-3.5 and GPT-4 APIs for batch processing. Specifically, we created the “GPT35” function to call the gpt-3.5-turbo-0125 API within Google Spreadsheet. Users can invoke this function by entering “=GPT35([prompt])” into a cell, enabling the intuitive batch processing of multiple titles and abstracts. Similarly, we established the “GPT4” function to access the gpt-4-0125-preview API.

Both the gpt-3.5-turbo-0125 and gpt-4-0125-preview have a parameter called “temperature,” which introduces “variability” in the responses—the higher the temperature, the greater the randomness, with a range between 0 and 2 [ 37 ]. As described later in this study, the decision to include or exclude records was delegated to GPT-3.5 and GPT-4. At the preliminary trials, it was observed that setting the temperature above 0 resulted in varying responses from one trial to another. In addition, setting the temperature above 0 can lead to unexpected responses. When instructed to respond with either “E” (for the exclusion) or “I” (for the inclusion), if the temperature is 0, the output will be strictly “E” or “I.” However, if the temperature is above 0, even if it is only 0.1, the response might be, for example, “The answer is ‘E’.” In light of these observations, and primarily to ensure reproducibility, this study fixed the temperature at 0 for all screenings. The Apps Script used in this study is shown in Multimedia Appendix 1 .

Process of Screening and Prompt Engineering

Generally, in a systematic review, a comprehensive examination is conducted on studies that address a relevant clinical question. After a comprehensive literature search is performed to identify all potential studies for review, each record is assessed to determine whether it addresses the clinical question [ 11 ]. In this study, we used either GPT-3.5 or GPT-4 to assess the inclusion or exclusion of relevant papers at each of the following three layers: (1) research design, (2) target population, and (3) intervention and control [ 11 ]. Records not deemed for exclusion at any of these layers were classified as “included.” We present the workflow of the process we conducted in Figure 1 .

The characteristics of the 2 systematic review papers [ 38 , 39 ] used in this study are summarized in Table 1 . The first paper by Takeshima et al [ 38 ] investigated the efficacy of bright light therapy in patients with bipolar disorder. In this study, the titles and abstracts of a total of 1381 records were initially screened in duplicate, with the task being divided between 2 pairs of independent evaluators. The first pair reviewed the initial 753 records, while the second pair assessed the remaining 628 records. Of these, 30 records were targeted for a full-text assessment, and eventually 6 records (encompassing 6 studies) were selected for meta-analysis. The second paper by Maruki et al [ 39 ] verified the difference in therapeutic effects between the usage of 2 types: second-generation antipsychotics (SGAs) and mood stabilizers (MSs), versus the usage of either type alone, targeting patients with bipolar disorder. In this study, the titles and abstracts of a total of 3146 records were initially screened in duplicate, with the screening divided between 2 pairs of evaluators. The first pair reviewed the initial 1694 records, while the second pair evaluated the remaining 1452 records. Of these, 96 records were targeted for a full-text assessment, and eventually 9 records (encompassing 5 studies) were selected for meta-analysis. We used the data on the inclusion or exclusion decisions of each human evaluator made prior to reaching a consensus among evaluators.

The screening process was divided into three layers: (1) research design, (2) target population, and (3) intervention and control. The prompts for each layer must be specifically tailored to each systematic review. At this point, manual prompt adjustments could lead to issues with reproducibility in future research. Therefore, in this study, we used GPT-4 (gpt-4-0125-preview, temperature=0) to automatically extract the information and generate the content for the prompts related to “research design,” “target population,” “intervention,” and “control.” The prompts used for extraction, along with the content defined for “research design,” “target population,” “intervention,” and “control,” are detailed in Textbox 1 . In this study, we extracted information by inserting the text from the “inclusion criteria” paragraph of the Methods section of each paper into the specified location in the prompt ( Textbox 1 ).

The structure of the prompts for each of the 3 layers is shown in Textbox 2 . Within these prompts, we specified that if a decision cannot be made, records should be considered potentially eligible for full-text assessment and not excluded. In this study, the information supplied to GPT-3.5 and GPT-4 was limited to the titles and abstracts of the records; details such as authors, their affiliations, or journal names were not included in the prompts.

In the screening process using GPT-3.5 or GPT-4, we initially verified whether the research design of all records satisfied the inclusion criteria. For records not excluded in the first layer, we subsequently confirmed whether the target population aligned with the inclusion criteria. Moreover, for records that were not excluded in the first and second layers, we assessed whether both the intervention and control groups met the inclusion criteria ( Figure 1 ).

• Research design: [insert your answer here]
• Target population: [insert your answer here]
• Intervention: [insert your answer here]
• Control: [insert your answer here]
• Research design: Randomized controlled trials (RCTs) at the individual or cluster level, including crossover studies reporting results from the first period.
• Target population: Patients with a clinical diagnosis of bipolar disorder (BD), type I or type II.
• Intervention: Any kind of light therapy, including 'light therapy,' 'bright light therapy,' 'phototherapy,' or chronotherapy in any intensity and color.
• Control: Sham treatment (e.g., low-intensity light, dim red light, or negative ion) or treatment as usual (no light treatment).
• Research design: Randomized controlled trials (RCTs) at the individual or cluster level, including crossover studies before crossover
• Target population: Participants diagnosed with bipolar I or II depression, including mixed features and/or rapid cycling.
• Intervention: Adjunctive therapy with second-generation antipsychotics (SGA) or mood stabilizers (MS) during baseline treatment with SGA or MS.
• Control: Adjunctive therapy with a placebo during baseline treatment with second-generation antipsychotics (SGA) or mood stabilizers (MS).
• Prompt for research design#Title and abstractTitle: [ Title of the record was inserted here ]Abstract: [ Abstract of the record was inserted here ]#Research design[ The ‘research design’ specified in Textbox 1 was inserted here ]#QueryYou are a researcher rigorously screening titles and abstracts of scientific papers for inclusion or exclusion in a review paper.Does the paper with the above title and abstract meet the specified research design? If yes, highly suspected, or difficult to determine, answer 'I'. If not, answer 'E'.#RulesYou can reply using only 'E' or 'I'.#Your answer:
• Prompt for target population#Title and AbstractTitle: [ Title of the record was inserted here ]Abstract: [ Abstract of the record was inserted here ]#Target population[ The ‘target population’ specified in Textbox 1 was inserted here ]#QueryYou are a researcher rigorously screening titles and abstracts of scientific papers for inclusion or exclusion in a review paper.Does the paper with the above title and abstract meet the specified target population? If yes, highly suspected, or difficult to determine, answer ‘I’. If not, answer ‘E’.#RulesYou can reply using only ‘E’ or ‘I’.#Your answer:
• Prompt for intervention and control#Title and abstractTitle: [ Title of the record was inserted here ]Abstract: [ Abstract of the record was inserted here ]#Intervention[ The ‘intervention’ specified in Textbox 1 was inserted here ]#Control[ The ‘control’ specified in Textbox 1 was inserted here ]#QueryYou are a researcher rigorously screening titles and abstracts of scientific papers for inclusion or exclusion in a review paper.Does the paper with the above title and abstract meet the specified intervention and control criteria? If yes, highly suspected, or difficult to determine, answer 'I'. If not, answer 'E'.#RulesYou can reply using only 'E' or 'I'.#Your answer:

Data Analysis

In this study, we analyzed the results from human evaluators of systematic review papers, comparing these with the records identified by GPT-3.5 or GPT-4. We considered the records included in the full-text assessment to be correct. We assessed the inclusion or exclusion decisions made by each human evaluator (before consensus was reached) against those determined by GPT-3.5 or GPT-4, focusing on sensitivity and specificity. Sensitivity was defined as the proportion of correctly identified eligible records for full-text assessment by human evaluators, GPT-3.5, or GPT-4. Formally, sensitivity is calculated as follows:

Sensitivity = True positives / (True positives + False negatives)

True positives = Number of records correctly identified as eligible

False negatives = Number of records incorrectly identified as ineligible.

Similarly, specificity was defined as the proportion of correctly identified ineligible records (for full-text assessment) by human evaluators, GPT-3.5, or GPT-4. Formally, specificity is calculated as follows:

Specificity = True negatives / (True negatives + False positives)

True negatives = Number of records correctly identified as ineligible

False Positives = Number of records incorrectly identified as eligible.

For records eligible for full-text assessment but excluded by either GPT-3.5 or GPT-4, we reviewed the title and the abstract to assess whether the exclusion decision was justified. Following this review, we recalculated sensitivity and specificity after adjusting for these justified exclusions. Furthermore, for records that were incorrectly excluded by GPT-3.5 or GPT-4, we conducted a narrative verification of the erroneous judgments by asking each LLM to explain the reasons behind their decisions. We modified the prompt used for screening ( Textbox 2 ) by replacing the “#Rules” statement with “Specify the reason for your answer.” This modification allowed GPT-3.5 or GPT-4 to provide their judgment results along with the underlying reasons.

Ethical Considerations

This study used only publicly available data from research papers and does not involve human subjects or personal data. Therefore, it does not require a human subject ethics review or exemption.

Results on Takeshima et al Paper

Figure 2 [ 38 ] shows the number of records excluded by GPT-3.5 and GPT-4 at each layer of research design, target population, and intervention and control, applied to records in the paper by Takeshima et al [ 38 ].

GPT-3.5 excluded 84 records at the research design layer, 877 records at the target population layer, and 0 record at the intervention and control layer, ultimately determining 420 out of 1382 records for inclusion. None of the 6 records (including 6 papers) that were included in the meta-analysis were excluded by GPT-3.5. The sensitivity for included records was 0.900 and the specificity was 0.709. Among the eligible records for full-text assessment, GPT-3.5 classified 3 (10.0%) records as excluded. Of these, the exclusion of 2 records by GPT-3.5 was justified, while the remaining 1 (3.3%) record was deemed to require full-text assessment ( Table 2 ). After adjustments for these justified judgments ( Multimedia Appendix 2 ), the sensitivity improved to 0.966 and the specificity remained at 0.710. For the one record that GPT-3.5 determined to be excluded at the target population layer, it was suggested that GPT-3.5 concluded that the record “included both bipolar disorder and unipolar mood disorder, which did not match the selection criteria.”

a Number of records for which exclusion was not justified.

GPT-4 excluded 589 records at the research design layer, 760 records at the target population layer, and 1 record at the intervention and control layer, ultimately determining 31 out of 1381 records for inclusion. None of the 6 records (including 6 papers) that were included in the meta-analysis were excluded by GPT-4. The sensitivity for included records was 0.806 and the specificity was 0.996. Among the eligible records for full-text assessment, GPT-4 classified 6 (20.0%) records as excluded. Of these, the exclusion of 5 records by GPT-4 was justified, while the remaining 1 (3.3%) record was considered to require full-text assessment ( Table 2 ). After adjustments for these justified judgments ( Multimedia Appendix 2 ), the sensitivity improved to 0.962 and the specificity remained at 0.996. GPT-4 included all 6 records (including 6 papers) that were included in the meta-analysis. For the one record that GPT-4 judged to be excluded at the research design layer, it was revealed that GPT-4 deduced that “although this study mentioned registration in an RCT, it investigated the associations between sleep, physical activity, and circadian rhythm indicators” (from the perspective of whether to include the study in the meta-analysis, GPT-4’s judgment is likely to be correct; however, considering the purpose of the initial screening, we determined that it would be appropriate to include the study).

Results of the Paper by Maruki et al

Figure 3 [ 39 ] shows the number of records excluded by GPT-3.5 and GPT-4 at each layer of research design, target population, and intervention and control, applied to records in the Maruki et al [ 39 ] paper.

GPT-3.5 excluded 220 records at the research design layer, 126 records at the target population layer, and 10 records at the intervention and control layer, ultimately determining 2790 out of 3146 records for inclusion. None of the 9 records (including 9 papers) that were included in the meta-analysis were excluded by GPT-3.5. The sensitivity for included records was 0.958 and the specificity was 0.116. Among the eligible records for full-text assessment, GPT-3.5 classified 4 (4.2%) records as excluded. None of these records’ exclusion by GPT-3.5 was justified, and all were considered to require full-text assessment ( Table 3 and Multimedia Appendix 2 ). For the 2 records that GPT-3.5 inferred to be excluded at the research design layer, it was revealed that GPT-3.5 determined that “although they were RCTs, either the individual or cluster level was not specified” for both records. For the 2 records that GPT-3.5 deemed to be excluded at the target population layer, it was suggested that GPT-3.5 surmised that “although the records involved bipolar disorder, they did not match the selection criteria due to the presence of comorbidities (one record had generalized anxiety disorder, and the other had alcohol dependence).”

GPT-4 excluded 1287 records at the research design layer, 503 records at the target population layer, and 830 records at the intervention and control layer, ultimately determining 526 out of 3146 records for inclusion. None of the 9 records (including 9 papers) that were included in the meta-analysis were excluded by GPT-4. The sensitivity for included records was 0.875 and the specificity was 0.855. Among the eligible records for full-text assessment, GPT-4 classified 12 (12.5%) records as excluded. Of these, the exclusion of 8 records by GPT-4 was justified, while the remaining 4 (4.2%) records were considered to require full-text assessment ( Table 3 ). After adjustments for these justified judgments ( Multimedia Appendix 2 ), the sensitivity improved to 0.943 and the specificity remained at 0.855. “For the one record that GPT-4 determined to be excluded at the target population layer, it was suggested that GPT-4 inferred that ‘although the record involved bipolar disorder, it did not match the selection criteria due to the presence of a comorbidity (alcohol dependence).’ For the three records that GPT-4 judged to be excluded at the Intervention and control layer, in each case, GPT-4 cited the reason for exclusion as ‘the intervention criteria are the addition of either SGA or MS to SGA or MS, but this study does not mention the use of SGA.’”

In the list used in the paper by Maruki et al [ 39 ], there were a total of 355 records where part of the title and abstract were corrupted into irrelevant Chinese characters (eg, “This was an eight窶陣eek, open窶人abel, prospective study”). Despite these errors, all cases could be appropriately discerned, likely due to the context-sensitive judgment capability of GPT-3.5 and GPT-4.

Comparison of GPT-3.5, GPT-4, and Human Evaluators

Both the study by Takeshima et al [ 38 ] and the study by Maruki et al [ 39 ] involved 2 individuals conducting screening for the initial segment, while a different set of 2 individuals was responsible for the screening of the latter segment. The sensitivity and specificity of human evaluators and GPT-3.5 and GPT-4 for each segment are shown in Table 4 . The adjusted results, in cases where the exclusion of GPT-3.5 or GPT-4 was justified, are shown in the numbers within parentheses ( Table 4 ).

a LLMs: large language models.

b Not applicable.

c Values after adjusting for cases where exclusion was justified.

Time and Cost Required for Screenings

In our Google Spreadsheet setup, both GPT-3.5 and GPT-4 managed to process approximately 110 records per minute across each of the 3 layers. Consequently, the estimated ideal completion time was between 20 and 30 minutes for the study by Takeshima et al [ 38 ], and between 60 and 80 minutes for the study by Maruki et al [ 39 ]. However, in practice, due to errors with the Google Spreadsheet and API, the screening process took about 1 hour for the study by Takeshima et al [ 38 ] and about 2 hours in total for the study by Maruki et al [ 39 ]. Furthermore, due to daily API call limits, the work had to be spread out over 3 days. The screening for these 2 studies incurred a total cost of US $59, with US $4 for calls to GPT-3.5 and US $55 for calls to GPT-4.

Principal Findings

This study demonstrates the use of a 3-layer screening method using GPT-3.5 and GPT-4 for title and abstract screenings in systematic reviews, highlighting its remarkable speed and sensitivity comparable with that of human evaluators. However, GPT-3.5 demonstrated low specificity for relevant records, rendering it less practical. In contrast, the use of GPT-4 showed both high sensitivity and specificity, particularly where adjustments for justified exclusions led to an improvement in sensitivity. Although achieving 100% sensitivity remained unattainable, a 3-layer screening method with GPT-4 may potentially be practical for use in the systematic review process and can reduce human labor.

Previous research demonstrating the effectiveness of automated screening using text mining has encountered sensitivity issues [ 20 - 29 ]. Specifically, the exclusion of important studies that should have been included in their meta-analysis [ 20 , 21 , 23 , 29 ], a limitation not observed in our approach, hampered their application to clinical practice. False negatives in machine learning–based screening can arise from several factors: complexity in research design, characteristics of the target demographic, types of interventions, complexity in selection criteria, a significant scarcity of relevant records within the data set (leading to data imbalance), and inconsistency in the terminology used for judgment [ 21 , 23 , 29 ]. Our method using GPT-3.5 or GPT-4 was able to address issues related to data set imbalance and terminology inconsistency, as we used the same prompt across records, and assess the inclusion or exclusion one by one. In addition, previous text mining screenings may not have effectively addressed garbled text, such as “open-label” mistakenly appearing as “open窶人abel” [ 40 ], an issue that LLMs can potentially mitigate through their attention mechanisms [ 41 ]. Moreover, the outstanding knowledge base of GPT-4 [ 6 , 7 ] likely helped address the complexity in research design, target demographics, and intervention, as well as selection criteria—areas where GPT-3.5 might have fallen short. These distinctions possibly account for the notable differences in specificity observed between GPT-3.5 and GPT-4. Recently, Guo et al [ 33 ] conducted title and abstract screening using GPT-4. Their approach diverges from our 3-layer method; it integrated inclusion and exclusion criteria within the context, generating decisions and reasoning through a single prompt. While we believe that our 3-layer method could potentially offer greater sensitivity than theirs, it remains difficult to definitively assert a significant improvement in sensitivity over the method by Guo et al [ 33 ], given the limited sample size and the differences in data sets. Tran and colleagues’ approach [ 34 ], despite using GPT-3.5, demonstrated remarkable sensitivity. It is important to note, however, that the manual creation of their highly effective prompt raises questions regarding its replicability and broader applicability.

Both human-conducted and LLM-conducted systematic reviews have their inherent pitfalls. Errors made by humans are inevitable, with their accuracy estimated to be around 10% [ 15 ], and slightly higher for false exclusions, at approximately 13%-14% [ 16 , 17 ]. These values represent the performance of experts in the relevant field, and the accuracy may be lower for individuals with less expertise or shallow screening experience; therefore, guidelines have recommended piloting and training the abstract screening team [ 12 ]. In this study, we observed that human evaluation in the paper by Takeshima et al [ 38 ] exhibited slightly more false negatives than that in the paper by Maruki et al [ 39 ]. Although the reasons for the judgment discrepancies were not investigated in this study’s data set, they may be attributed to the larger volume of records screened [ 14 ] and the potentially more complex and challenging research question in the paper by Maruki et al [ 39 ]. Using 2 reviewers to screen records can significantly lower the likelihood of false negatives [ 16 ] and has been recommended [ 11 , 13 ]. Yet, simultaneously, there has been a case that the systematic review screenings, albeit rare, are conducted by a single reviewer, because of time constraints [ 13 , 42 ]. Hence, the unavoidable errors and substantial time and effort required for screening represent significant drawbacks of human screening in systematic reviews [ 10 , 13 ].

Conversely, methods using LLMs also present several drawbacks. One primary concern is their susceptibility to misinformation and quality issues inherent in their training data [ 43 ]. Notably, in this study, the specificity of the GPT-3.5 screenings in Maruki et al [ 39 ] paper was markedly low. While the causes are not definitive, this may be attributed to an insufficient understanding of bipolar disorder, MSs, and second-generation antipsychotics. Tran and colleagues [ 34 ] incorporated relevant knowledge into their manually created prompts; it might have enhanced sensitivity but not specificity; and this could also be due to GPT-3.5’s knowledge limitations. Furthermore, the decision-making processes of LLMs lack transparency, making them difficult to interpret [ 43 ]. This lack of interpretability is compounded by the “grounding problem,” where LLMs struggle to grasp concrete facts and real-world scenarios due to their lack of real-world experiences and sensory input [ 1 , 44 ]. We attempted to verify incorrectly excluded records by querying GPT-3.5 and GPT-4 with the original screening prompts, their responses, and justifications. Our findings revealed that GPT-3.5’s lower accuracy was primarily due to a lack of knowledge about the target domain, while GPT-4’s incorrect exclusions were mainly due to misinterpretations of the inclusion criteria. These findings highlight the ongoing challenges in understanding and interpreting the decision-making processes of LLMs. Although GPT-4 demonstrates advancements in comprehension, factuality, specificity, and inference, it is still more susceptible to factual errors [ 45 ]. In addition, it has been suggested that LLMs’ accuracy diminishes with longer prompts [ 46 ]; lengthy abstracts might have contributed to decreased accuracy in decision-making. A potential future risk is that the normalization of AI-based judgments could result in the oversight of human expert verification, potentially diminishing the quality of systematic reviews.

On the positive side, compared with the human screening time reported in previous studies [ 10 ], our method enabled remarkably faster screening. Although our approach uses a 3-layer structure, which might seem time-consuming at first glance, by limiting GPT-3.5/GPT-4 responses to “E” (Exclude) or “I” (Include), we efficiently screened a large volume of records in batch. Unlike humans, LLMs do not experience fatigue and subsequent decline in performance; moreover, they are presumed to have better reproducibility in their judgments. While using GPT-4’s API comes with associated costs [ 36 ], the increased efficiency compared with human effort more than compensates for these expenses. Using LLMs for title and abstract screening could also enable screening a much larger number of records, previously deemed impractical due to time limitations. Our 3-layer method using GPT-4 exhibits high sensitivity and a useful level of specificity and yet opportunities for further refinement exist. Future studies could enhance accuracy through methods such as optimizing prompts [ 47 ] and integrating multiple LLMs for decision assessment [ 48 ], which may contribute to higher precision. In the meantime, swift advancements in LLM technology are set to continuously evolve; future breakthroughs in LLMs may readily overcome our current challenges—possibly, only by a simple prompt.

Limitations

This study has some limitations. First, the 2 systematic reviews used in this investigation [ 38 , 39 ] were confined to clinical studies within psychiatry, limiting the generalizability of our findings. In addition, the sample size was small, and the investigation remained exploratory, with the results lacking statistical substantiation. Future studies should aim to replicate these findings across a broader range of medical fields and specialized domains to enhance their applicability and reliability. Second, the artificial intelligence industry is progressing rapidly, with information becoming obsolete within a matter of months or even weeks. The models we used in this study, gpt-3.5-turbo-0125 and gpt-4-0125-preview, are currently the most up-to-date. However, updates to these models might alter screening outcomes. Third, to ensure consistency in our findings, we set the temperature parameter to 0. However, a temperature of 0 does not always guarantee absolute uniformity in output sentences [ 35 ]. However, our observations indicate no variation in results across multiple tests with the same model in this study. Fourth, this study did not investigate the discrepancies in screening results between GPT-3.5 and GPT-4, nor did it examine the impact of prompt variations on performance. In addition, this research did not directly compare the performance of the proposed approach with existing systematic literature review strategies. Furthermore, this study was not designed to explore the risks associated with using LLMs for screening purposes. Finally, gpt-3.5-turbo-0125’s training data include information up to September 2021, whereas gpt-4-0125-preview’s training data extend to December 2023 [ 35 ]. Consequently, the systematic review paper by Takeshima et al [ 38 ] might have been incorporated into GPT-3.5’s training data set, with both systematic review papers possibly included in GPT-4’s data set. Nevertheless, as the study’s prompts did not explicitly reference these reviews, we consider that their impact is minimal.

Conclusions

We developed a practical screening method using GPT-3.5 and GPT-4 in the title- and abstract-screening process of systematic reviews. Our 3-layer method not only achieved better sensitivity for relevant records than previous machine learning–based screening methods [ 20 , 21 , 23 , 29 ] but also demonstrated a remarkable potential to reduce human reviewers’ workload significantly. Although GPT-3.5 showed lower specificity, which may limit its applicability, the use of GPT-4 within our method yielded sensitivity comparable with human evaluators, making it suitable for use in systematic review screenings. Despite the focus on psychiatric fields and the small sample size of our study, our findings highlight the potential for broader application. We emphasize the importance of further validation across multiple domains to establish a universal screening methodology. Concurrently, developing more effective approaches in response to the advancing capabilities of LLMs is warranted in future research.

Acknowledgments

This work was supported by the Japan Society for the Promotion of Science (JSPS) KAKENHI (grant 22K15778). During the preparation of this work, the authors used ChatGPT (GPT-4 and GPT-4o, by OpenAI), Claude (Claude 3 Opus, by Anthropic), and Gemini (Gemini 1.5 Pro, by Google) to enhance the readability and proofread the English text. After using these services, the authors reviewed and edited the content as needed and took full responsibility for the content of the publication.

Conflicts of Interest

None declared.

Script for the Google Spreadsheet.

Records eligible for full paper screening but excluded by GPT-3.5 or GPT-4.

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Abbreviations

Edited by S Ma; submitted 14.09.23; peer-reviewed by D Fraile Navarro, T Nguyen, A Nakhostin-Ansari; comments to author 23.01.24; revised version received 10.03.24; accepted 25.06.24; published 16.08.24.

©Kentaro Matsui, Tomohiro Utsumi, Yumi Aoki, Taku Maruki, Masahiro Takeshima, Yoshikazu Takaesu. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 16.08.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research (ISSN 1438-8871), is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

Research Strategy

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• Azhdar Karami 3 , 4  

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This chapter sets out the various steps that are necessary in executing this study and thereby satisfying its objectives. It aims to explain in detail all aspects of the research, with particular reference to all of the key theoretical and practical issues involved. This chapter discusses the research design and methodology and the survey. Various research philosophies and approaches presented in literature will be discussed with focuses on their application to this study. This chapter is composed of seven sections which cover the research objectives and questions, research philosophy, research approach, research strategies, research choices, research time horizons, and research techniques and procedures. Each sub-section of this chapter covers one of these aspects of the research. The first sub-section addresses the objective and questions of the study. The first sub-section addresses the objective and questions of the study. The second sub-section presents the research methods and strategy and includes research philosophy, research approach, research design, data collection process and constructing the questionnaire. The third sub-section addresses the conceptual framework and research variables. The fourth sub-section presents the characteristics of SMEs. The fifth sub-section reveals the proposed model and hypotheses. The sixth sub-section presents the research choice and data analysis.

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• Published: 19 June 2024

Why do patients with cancer die?

• Adrienne Boire   ORCID: orcid.org/0000-0002-9029-1248 1   na1 ,
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Cancer is a major cause of global mortality, both in affluent countries and increasingly in developing nations. Many patients with cancer experience reduced life expectancy and have metastatic disease at the time of death. However, the more precise causes of mortality and patient deterioration before death remain poorly understood. This scarcity of information, particularly the lack of mechanistic insights, presents a challenge for the development of novel treatment strategies to improve the quality of, and potentially extend, life for patients with late-stage cancer. In addition, earlier deployment of existing strategies to prolong quality of life is highly desirable. In this Roadmap, we review the proximal causes of mortality in patients with cancer and discuss current knowledge about the interconnections between mechanisms that contribute to mortality, before finally proposing new and improved avenues for data collection, research and the development of treatment strategies that may improve quality of life for patients.

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Introduction.

The phrase ‘metastasis accounts for 90% of cancer deaths’ is one of the most widely used in cancer research, yet it is overly simplistic, imprecise and it is difficult to find any primary analysis supporting the statement. Although patients with metastatic disease are overwhelmingly more likely to die than patients with non-metastatic cancer 1 , 2 , the determinants of cancer mortality are multifaceted and frequently involve dysfunction of multiple interconnected systems within the body. Understanding the mechanisms underpinning the causes of mortality, and subsequently intervening, has the potential to make cancer a less destructive disease, improving both the quality and length of life for patients with cancer. However, systematic analyses of the acute and root causes of mortality in patients with cancer are scarce, in part because death certificates rarely record enough information to understand the exact reason why the patient died beyond them having a malignancy. Causes of death may be simply listed as ‘metastatic carcinoma’ or ‘complications of cancer’, which give little insights into why a patient actually died. Potentially concomitant comorbidities are also not fully recorded. Even in cases in which the cause of death may be attributed to a single event, for example, a  thromboembolism , the underlying cause of that specific event may be complex. Indeed, metastatic cancer leads to perturbed function of multiple organ systems, and importantly, not just the organs to which disease has spread. This is probably due to the exuberant activation of local and systemic inflammatory, tissue repair and immune-suppressive programmes.

A simple view would be that death from metastatic disease correlates with the burden of disease. However, evidence suggests that the situation is more complex, with many factors influencing how metastases impact vital functions and ultimately lead to death. First, metastases to different organs will lead to different impacts on overall health. For example, brain metastases can lead to dysfunction of the central nervous system 3 , whereas peritoneal metastases may cause obstruction of the bowel 4 . In addition, the size or extent of metastases may not necessarily correlate with dysfunction of the organ where it is located 5 . Second, the production of the molecular mediators of organ dysfunction can vary between metastases and cancers of different origins. Third, individual patient characteristics such as age, sex, overall health, pre-existing comorbidities, genetics and socio-economic status vary 6 . Together, these factors directly influence the course of and physiological response to metastatic disease and can have profound indirect effects by limiting available treatment options and/or the ability of patients to tolerate or complete all intended treatment 7 , 8 . To understand why patients with cancer die, a closer examination of the factors contributing to mortality in patients with cancer and a dissection of the intricate web of causes that shape the frequency and dynamics of death are required.

Death may be related to an acute event, but the underlying mechanisms which trigger it may be modifiable or even preventable. In addition, other deaths may be the end stage of a continuum of deterioration, allowing the possibility of targeted intervention to improve quality of life. Moreover, it has been noted that early palliative care improves survival 9 . Ultimately, increased understanding of the processes occurring in patients with advanced disease should lead to improved strategies to minimize ill-health and suffering at the end of life. Coupled to this, patients and those around them should be enabled to have essential discussions about their wishes and preferences, minimizing potentially inappropriate treatments and maximizing quality of life 10 . Therefore, in this Roadmap, we briefly review data considering the immediate causes of mortality, highlight the intricate interconnections between different aspects of patient deterioration and conclude with recommendations for future studies of late-stage cancer that may shed new light on this important aspect of cancer biology and medicine.

Acute events leading to mortality

Although some cancers can be considered a chronic condition, with many patients living with their disease for years, the immediate cause of mortality can often be an acute event. Here, we briefly summarize common acute events leading to death in patients with cancer (Fig.  1 ). Although it is not possible to precisely determine, it is likely that the acute causes discussed subsequently may account for up to half of cancer deaths 11 , 12 . Immediate causes of mortality in other patients are less clear, with a more gradual deterioration typically occurring in vital organ systems (Fig.  1 ).

This schematic shows organs that frequently become dysfunctional in patients with late-stage cancer.

Vascular coagulation and cardiac failure

Patients with cancer are at an elevated risk of thromboembolism, which may trigger respiratory failure, fatal strokes, heart failure or myocardial infarction 13 . In some cases, disseminated intravascular coagulation can lead to thrombotic obstruction of small and midsize vessels leading to organ failure 14 . Haemorrhagic complications from depletion of platelets, via either immune or non-immune mechanisms 15 , 16 , and reduced levels of coagulation proteins can also be life-threatening 14 .  Congestive heart failure can also be a proximal cause of mortality, although the underlying causes are complex and include loss of cardiac muscle (associated with cachexia), shifts in intravascular fluid status and thromboembolic events 17 . Interestingly, bone metastases are particularly associated with cardiovascular problems, although the underlying mechanism remains unclear 18 . Comorbidities affecting the cardiovascular system may also make patients more prone to such events. Spatial occlusion of or invasion into vessels by cancer metastases can also lead to failure in blood supply or catastrophic haemorrhage 19 , 20 , 21 , 22 .

Displacement, functional impairment or obstruction of vital organs

The volume of disease may impair the function of a vital organ. This can be the case with brain metastases and glioblastoma or other primary brain cancers, with extensive invasion, brain herniation or oedema resulting in  midline shift or increased intracranial pressure irreversibly compromising brain function 22 , 23 , 24 . In addition, patients may develop seizures, which, if uncontrolled, can result in death 25 , 26 . However, this does not apply to all brain metastases, with  leptomeningeal metastases having minimal impact on intracranial pressure and brain structure; instead, these commonly obstruct cerebrospinal fluid flow and/or affect nerve function resulting in  hydrocephalus , deterioration of neurological function and death 26 .

Large lung metastases may impair the essential function of gas exchange. However, patients with miliary-like disease — characterized by nodules too numerous to count — can live with extensive disease in an organ with surprisingly little impact on function until a hard-to-predict tipping point is reached, which is then followed by rapid deterioration 27 . As with brain metastases, the volume of disease is often not sufficient to account for organ failure, as even a relatively small volume (<100 ml lung metastases volume compared with 4–5 l total lung volume) can be fatal 28 .  Lung oedema and pleural effusion are additional common contributors to death. Oedema may be caused by other pathologies such as infection or heart failure, whereas pleural effusion may be related to the presence of disease within the pleura as opposed to total tumour volume 28 , 29 .

Bowel obstruction can be a cause of mortality, especially in patients with peritoneal disease as found in particular in ovarian, colorectal and gastrointestinal cancers 30 . Both liver and kidney failure will also cause death in patients with cancer. Reasons for the failure of these organs include obstruction of the bile duct or ureters by metastases, therapy-induced toxicity leading to compromised normal organ function (discussed subsequently) and reduced tissue perfusion owing to hypotension or dehydration 31 , 32 , 33 , 34 . In addition, sepsis can result from obstruction of the bile ducts or ureters, which occurs unpredictably and often progresses rapidly leading to multiple organ failure and ultimately death.

Bacterial infections are the most common infection in patients with cancer, owing to impaired immune systems resulting from both the cancer itself and certain cancer treatments (discussed in detail subsequently), which induce myelosuppression and leukopenia. Patients with cancer can have an elevated risk of opportunistic viral, fungal and protozoal infections, which would typically be considered mild in healthy individuals, but which can cause serious life-threatening complications in those with cancer. Pneumonia and other lung infections leading to respiratory failure are often listed as causes of mortality in patients with cancer 35 , 36 . One of the most striking recent examples of this is the increased mortality observed in patients with cancer, particularly those with haematological cancers, who succumbed to COVID-19 compared with the general population 36 , 37 .

Paraneoplastic syndromes

Paraneoplastic syndromes are a group of rare disorders that can occasionally cause irreversible damage to critical organs and death. They are most associated with lung, breast, ovarian and lymphatic cancers, causing tissue or organ dysfunction at sites distinct from the location of the tumour 38 . Various mechanisms underpin paraneoplastic syndromes, including the inappropriate production of cytokines, hormones and antibodies. For example, excess parathyroid hormone-related protein (PTHRP) production by tumours can lead to hypercalcaemia 39 , 40 . Inappropriate anti-diuretic hormone production is commonly associated with small-cell lung cancer resulting in  hyponatraemia 41 . Furthermore, some neuroendocrine pancreatic tumours (insulinomas) secrete large amounts of insulin 42 . Tumours can also trigger the aberrant production of autoantibodies leading to disorders such as  Lambert–Eaton myasthenic syndrome (LEMS) or anti- N -methyl- d -aspartate receptor (NMDAR) encephalitis and myasthenia gravis 43 . Although treatment can usually manage the symptoms, in a subset of cases the syndromes cannot be controlled and are fatal 38 .

Therapy-induced toxicity

Although therapies are developed and administered with the intent of primarily targeting the tumour, almost all have some detrimental impact on normal tissue function. In some cases, the unintended consequences of therapy can be life-threatening. Autoimmune reactions resulting from targeting immune checkpoints can have fatal consequences, including  myocarditis and encephalitis 44 , 45 , 46 . Chemotherapy can lead to death as a result of acute neutropenic sepsis 47 . Depletion of platelets because of therapy can lead to fatal bleeding 16 . Arrhythmias, cardiomyopathy and coronary vasospasm are also a cause of death related to some anticancer treatments such as 5-fluorouracil and capecitabine 48 , 49 , 50 . The long-term detrimental effects of some therapies are discussed in detail subsequently.

Underlying causes

Determination of the proximal cause of mortality prompts further questions around the underlying factors giving rise to lethal pathology and ultimately how metastatic cancer triggers or accelerates those factors. In this section, we consider how chronic disruption of three major physiological organ systems is perturbed in patients with cancer and how these might contribute to mortality.

The immune and haematopoietic systems

In patients with cancer, the immune system becomes progressively less able to mount effective responses to infectious challenge, a phenomenon often generically termed ‘immune exhaustion’ (this usage is distinct from the more specific usage of immune exhaustion as a failure of tumour-reactive T cells to function). As a result, patients with metastatic disease have increased susceptibility to a wide range of infections and typically suffer more severe consequences than would otherwise be observed in healthy individuals 51 . Multiple mechanisms contribute to the reduced capability of the immune system to respond to infection. The presence of cancer cells in diverse organs triggers similar cellular and molecular events to wound responses 52 . The production of cytokines including interleukin 6 (IL-6), granulocyte colony-stimulating factor (GCSF) and granulocyte–macrophage colony-stimulating factor (GM–CSF), both by tumour cells and by other cells of the tumour microenvironment (TME), perturbs haematopoiesis leading to altered subsets of leukocytes 53 . Although, in the short term, this may have limited consequences on the ability of the body to respond to other challenges, prolonged disruption to haematopoiesis can strain the ability of haematopoietic stem cells (HSCs) to generate sufficient cells of the right type to cope with infections, with increased myeloid-to-lymphoid cell ratios.  Clonal haematopoiesis can be increased in patients with cancer, with myeloid skewing of immune cells and overall myeloid-mediated immune suppression and diminished naive T cell reservoirs 53 . Reduced production of platelets and altered iron metabolism leading to compromised oxygen carrying by red blood cells is also observed in many patients 54 . Other problems such as  immunoparesis can arise, with a high frequency observed in patients with multiple myeloma 55 .

T cell responses to infection are impaired in the presence of cancer with decreased proliferation and expression of granzyme B typically observed 56 . The chronic stimulation of T cells with neoantigens arising from ongoing mutational processes may also contribute to their weakened functionality. Moreover, immune surveillance of tumours inevitably selects for the production of immune suppressive factors by cancer cells, which further compound the issue 57 . Once again, comorbidities leading to either immune suppression or autoimmunity can intersect with the detrimental effects of cancer on the immune system.

Other consequences of cancer can indirectly result in an increased likelihood of infection. For example, vessel obstruction from cancer results in decreased flow of fluids such as bile, urine and lymph, creating environments in which bacteria can thrive 58 . Blockage of the bronchial tree can lead to pneumonia 59 . The invasive phenotype of cancer can result in  fistula formation (for example, rectovaginal in colorectal cancer), which enables bacteria to invade 60 . Furthermore, patients are often rendered bedbound or have limited mobility as cancer progresses, resulting in an increased chance of infections through decreased respiratory ventilation and atelectasis , as well as pressure sores and oedema 61 .

Disruption to haematopoiesis can also contribute to defects in coagulation and haemostasis . Elevated platelet numbers, termed thrombocytosis, are found in patients with cancer and are correlated with higher mortality 62 . The altered inflammatory cytokine milieu caused by the tumour may promote megakaryopoiesis, potentially through increasing thrombopoietin (TPO) production by the liver, and leading to higher platelet numbers. The risk of clotting can be further increased by the production of  tissue factor , which is responsible for initiating the clotting cascade, by tumour cells 63 . These mechanisms increase the likelihood of fatal thromboembolisms 63 .

Iatrogenic effects also have a role in the reduced immune function in patients with cancer. Cytotoxic therapies interfere with the proliferation and division of haematopoietic stem cells and can leave the immune system unable to mount effective responses to pathogens, leading to mortality 64 . In severe cases, pancytopenia results, marked by a substantial decrease in all three major blood cell lineages (red cells, white cells and platelets) 65 . This can lead to severe anaemia, increased infection susceptibility and increased likelihood of bleeding 47 , 66 , 67 . In other cases, more limited subsets of haematopoietic cells are affected. Thrombocytopenia — low platelet levels — leads to hypocoagulation and elevates the likelihood of haemorrhage 66 . Therefore, during cancer development and treatment, haemostasis mechanisms may be either augmented or attenuated; in both cases, the result is less predictable and less well-controlled coagulation. Neutropenia — low neutrophil levels — renders patients less able to fight infection and contributes to cancer mortality from infections that in many cases are thought to arise from resident mucosal flora 68 . Treatments, including chemotherapy and radiotherapy, often result in the breakdown of mucosal barriers (for example, oral mucositis) resulting in higher numbers of infections from pathogens, which normally reside on these surfaces 69 . In addition, corticosteroids, which are often given to alleviate symptoms or manage toxicity, can also contribute to the suppression of immune responses and compound the risk of infections in patients 70 . Clonal haematopoiesis, which as mentioned earlier is already more frequent in patients with cancer, can be further increased by chemotherapy 71 . More generally, cancer therapies can increase ageing-associated processes and reduce organ function 72 . Opioid pain relief administered to those with late-stage disease can also suppress the function of various bodily systems 73 . Finally, infections can arise owing to the insertion of drains and stents, or central venous catheters (CVCs; also known as lines) for the delivery of therapies. Infections from such lines are estimated to be around 0.5–10 per 1,000 CVC-days 74 , 75 .

Immunotherapies present a different set of immune complications from conventional therapies. These primarily relate to over-activation of the immune system leading to autoimmunity and, in some cases, cytokine storms that are treated with anti-cytokine therapies such as tocilizumab, anakinra and ruxilitinib, all of which can further suppress the immune response 76 . However, deaths attributable to autoimmune side effects of immune checkpoint inhibitors are rare (<1% in an analysis of more than 8,000 patients) especially if toxicity is managed promptly 77 , 78 . Colitis is a frequent problem, with disruption to colonic barrier function leading to increased susceptibility to perforation, which can be life-threatening.  Guillain–Barré syndrome , hepatitis and myocarditis are also causes of immune checkpoint inhibitor-related deaths 79 , 80 , 81 . Once again, high-dose corticosteroids are the main first-line treatment to manage autoimmune side effects in patients receiving immunotherapy and can lead to suppression of the immune system.  Hyperprogressive disease is observed in some patients following immunotherapy, the reasons for which are still being delineated, but there is probably a role for innate lymphoid cells releasing pro-growth cytokines 82 . Cell-based immunotherapies can also lead to disrupted bone marrow function and subsequent myelosuppression 83 .

The nervous system

The brain serves as a central nexus, orchestrating all vital functions. It is the hub of thought processes, emotions and sensory perception and regulates, directly or indirectly, everything from heartbeat and breathing to appetite. In addition to physical disruption of brain structure and intracranial pressure (discussed earlier) 84 , brain metastases impact the nervous system in multiple ways. Tumours in the brain or its surrounding tissues can substantially impair neural connections, leading to cognitive deficits, motor and sensory dysfunction, and even personality changes 84 , 85 , 86 . Interactions between brain metastases and neurons lead to changes in cortical function 87 , 88 , 89 . Even in regions of the brain without overt metastases, neuro-excitability can be increased, leading to changes in cognition, alertness and mood 90 . Tumours can slow the posterior dominant rhythm, leading to reduced alertness, loss of working memory and deterioration of quality of life 91 . Circadian rhythms are also impacted, leading to problems in memory and sleep, which is vital for the repair processes of the body that are essential for overall health and functioning 92 . Ultimately, many of these changes are not sustainable long-term. How these changes may lead to death is unclear, but they may follow similar trajectories to those in patients with dementia.

Brain function can also be disrupted in patients without brain metastases, with autonomic nervous system dysfunction often reported 93 . Intriguingly, anhedonia — a lack of ability to experience pleasure — occurs in many patients 94 . The mechanistic causes of this are unclear, but it is not restricted to patients with brain metastasis suggesting that circulating systemic factors may play a role. The wider effects of metastatic cancer on the mental well-being of a patient are discussed in Box  1 . However, beyond an effect on well-being, the disruption of brain function can contribute to anorexia, and reduced nutrition can influence many other physiological and pathophysiological processes 95 , 96 .

The role of the  peripheral nervous system in cancer-related death is not well described. Although the burgeoning field of cancer neuroscience provides evidence that the efferent system can support local and metastatic tumour growth 97 , 98 , 99 , at this time, it is unclear whether the reverse is also true. As mentioned earlier, there is clear evidence of autonomic nervous system dysfunction in patients with cancer 93 , raising the possibility that cancer-mediated interruption of afferent impulses might impact overall survival. Further studies are needed to explore this possibility.

Box 1 Psychosocial and societal factors contributing to the deterioration of patients with late-stage cancer

Psychological and social factors can have major and wide-ranging impacts on patients with incurable cancer. This manifests in more than threefold higher suicide rates 145 , 146 , 147 . Of note, these rates were further exacerbated in less advantaged sociodemographic groups 148 , arguing that financial issues and possibly health-care access are linked to suicide in patients with cancer. However, psychological symptoms are far more extensive than those captured in studies of suicide. Anhedonia and depression are frequent in patients with cancer, impacting their overall well-being, treatment adherence and outcomes including mortality 149 . These psychological challenges often intertwine with physical symptoms, compounding the burden of each 150 . Several studies have linked stress-related psychosocial factors to cancer mortality 151 , with recent work beginning to uncover the cellular and molecular mechanisms at play 152 .

Research on the psychosocial aspects of cancer care, including emotional and cognitive well-being, remains under-emphasized. Barriers to the integration of psychosocial care into cancer care include stigma, difficulty identifying substantial distress, limited access to evidence-based psychosocial treatments and concerns about cost 153 . Yet, an integrated system of psychosocial care including population-based screening and targeted treatment and access to good-quality palliative care improves emotional wellbeing 154 and physical symptoms 155 and is likely to be cost-saving 156 . A deeper understanding of the mechanisms underlying neuropsychological systems and insights into how metastatic disease impacts the physiological and chemical axes of the brain will be crucial. Such insights could inform tailored interventions, therapies and support structures that address the emotional toll of cancer, enhancing the holistic care approach and improving quality of life. Expanding psychosocial research can help bridge gaps in addressing mental health in patients with cancer, ultimately improving quality of life of patients during and after treatment 146 , 147 .

Metabolism and cachexia — catabolic effects of cancer

The presence of metastases presents altered energetic and anabolic demands on the body, leading to detrimental imbalances in metabolism 100 . Progressive and involuntary loss of body weight — termed cachexia — is a widespread multiorgan phenomenon commonly seen in patients with metastatic cancer 100 , 101 . This complex syndrome is characterized by a net negative energy balance, driven by the combination of increased energy expenditure and catabolism, with reduced appetite and caloric intake. A persistent decrease in nutrient intake is a key component across patients with many different cancer types, leading to breakdown of host tissues, with the degree of loss of adipose tissue and muscle mass varying between patients and among different cancer types 102 . However, the contribution of increased energy expenditure (as a result of tumour burden) is less clear.  Sarcopenia may be particularly prominent in some patients, possibly representing an independent pathology from other more global tissue wasting phenotypes, and in extreme cases, loss of cardiac or intercostal muscle mass can be fatal owing to insufficient cardiac or respiratory function, respectively 103 , 104 . These events have also been observed in the context of extreme starvation in patients with non-cancer conditions; for example, anorexia nervosa, in which cardiac dysfunction, in particular bradycardia and sinus pauses, can cause pulseless electrical activity and death 105 , 106 . Electrolyte disturbances and hypoglycaemia that are often observed in cases of severe malnutrition may exacerbate the risk of such arrhythmias 105 . Cachexia also has effects on other organs and tissues, including the brain and immune system 107 . Compromised immune function is a major consequence of starvation-induced tissue wasting 108 and suggests that altered systemic metabolism leading to, or associated with cachexia, may be a contributor to the immune dysfunction present in some patients with cancer 108 . Conversely, several studies have shown that both the brain and immune system can contribute to cachexia 100 , 101 .

Cachexia is multifactorial and has many potential causes. In some limited cases, tumour metabolism leads to systemic changes that increase energy usage. For example, high levels of lactate secretion by tumours can trigger the liver to convert lactate back to glucose, which requires energy input — termed the Cori cycle 109 . Such cycles can increase metabolic demand on the liver leading to further perturbation of liver function. However, cachexia does not correlate with disease volume in many cancer types 110 . Therefore, it is hard to reconcile a model in which the energetic and anabolic demands of the volume of disease are the main trigger for cachexia. Numerous studies have begun to reveal the possible molecular underpinnings of cachexia in some cancer types. Disruption of signalling by transforming growth factor-β (TGFβ) and related ligands is a recurring theme 111 , 112 , 113 . For example, circulating growth/differentiation factor 15 (GDF15; also known as MIC1), a highly conserved member of the TGFβ superfamily, is a known mediator of anorexia and weight loss, and increased circulating levels of this molecule in patients with lung cancer have been shown to correlate with cachexia development 114 . Clinical trials are currently underway to determine whether blockade of GDF15 ameliorates cachexia 115 . TGFβ itself can also promote muscle loss via the induction of myostatin 116 , and the induction of signalling by activin — another TGFβ superfamily ligand — can also have similar effects on muscle mass 117 , 118 . Furthermore, modulation of ryanodine receptor 1 (RYR1) downstream of TGFβ can perturb sarcomere organization and thereby lead to muscle weakness 111 . As such, preclinical studies have demonstrated the potential utility of TGFβ blockade in preventing cachexia 112 .

Elevated levels of cytokines, including tumour necrosis factor (TNF), IL-1 and IL-6, can also have roles in cachexia 119 , 120 , 121 . TNF induces multiple aspects of cachexia 122 . Muscle wasting is promoted through increased TNF and downstream nuclear factor-κB (NF-κB)-dependent ubiquitin-mediated proteolysis of muscle protein 123 , 124 . IL-6 triggers muscle loss through both NF-κB-dependent and JAK/STAT-dependent mechanisms 120 . Lipid metabolism is impacted by TNF reducing the expression of lipoprotein lipase and free fatty acid transporters, thereby reducing the accumulation of fat 125 . TNF can also reduce appetite through the production of  corticotropin-releasing hormone (CRH). IL-1, which triggers similar proximal changes in cell signalling to TNF, can activate many of the same processes 125 . It is also interesting to note that TGFβ, IL-1 and IL-6 are associated with programmes in cancer cells that drive metastasis, which could potentially explain why metastatic disease is linked to cachexia more strongly than the presence of primary disease alone.

Whole body dysfunction

Although consideration of different organ systems is useful for highlighting some of the key events contributing to cancer mortality, the interconnected nature of body systems and the pleiotropic characteristics of the molecular mediators at play mean that it is essential to consider whole body dysfunction when thinking about causes of cancer mortality. Furthermore, such analyses may explain cancer deaths without an acute proximal cause. As discussed earlier, cytokines with potent effects on the immune system, as well as effects on appetite, can be contributors to cachexia. Therefore, it is unsurprising that tumours impact both immune and metabolic function. The immune and nervous systems are highly sensitive to metabolite availability; for example, the brain has a high demand for glucose 108 , 126 . Several factors, including lactic acid production and kidney dysfunction, can lead to life-threatening systemic acidosis in patients with cancer, particularly haematological malignancies with high cell turnover 127 . These can be further exacerbated upon initiation of cytotoxic therapy resulting in  tumour lysis syndrome which can be fatal 128 . Consequently, metabolic perturbations and cachexia impact these systems. Over time, the cumulative stress of metabolic alterations caused by metastases, chronic changes in the level of cytokines, constant generation of tumour (neo)-antigens, aggressive therapies and incidental infections lead to exhaustion of the adaptive immune system and hamper the regenerative capacity of many organ systems with debilitating effects 18 . This multifaceted burden can ultimately trigger a body-wide shutdown leading to death.

Are mortality causes cancer-specific?

Although a subset of mortality causes are cancer-specific, such as metastatic invasion compromising specific organ function, the progressive and interconnected deterioration of multiple organ systems probably underlies many cancer-related deaths. This may be further influenced by interaction with other comorbidities. Of note, similar progressive deterioration is sometimes observed in the context of chronic infection and inflammation, with both cachexia and immune exhaustion associated with diseases such as tuberculosis (TB) and human immunodeficiency virus (HIV) infection 129 , 130 , 131 . This raises the question of whether the causes of death in patients with cancer are specific to cancer, or whether cancer (or any other chronic disease) is simply an accelerant of ageing processes occurring in healthy individuals. This hypothesis has practical implications because, if proven, it would suggest that lessons and approaches from other disease contexts could be readily transferable to patients with metastatic cancer. For example, the targeting or modulation of senescent cells is an active area of anti-ageing research, and numerous preclinical studies have indicated that similar strategies can attenuate the systemic effects of cancer 132 , 133 , 134 .

Recommendations

One goal of this Roadmap is to propose ways to improve our understanding of why patients with cancer die and thereby develop better strategies to ameliorate symptoms and prolong life with good quality. To this end, we propose that the following steps would be useful (Fig. 2 ).

This scheme shows how recommendations can interlink to provide both an improved understanding of the underlying biology behind late-stage cancer and strategies to improve quality of life of patients.

Improved records and reporting

It is notable that systematic reviews of the precise causes of cancer mortality are infrequent. This gap in knowledge, and recognition that this is often simply not known, is a major hindrance to learning and progress. Although improved accuracy of reporting on death certificates would be desirable, it would require a shift in longstanding clinical habits and may not be easily achievable in health-care systems under strain. Nonetheless, we advocate for locally enacting more consistency in death certificates, with specific acute causes included in addition to the underlying cause of cancer where known. Palliative care primarily focuses on symptom control for patients while balancing the potential benefits and burdens of additional diagnoses. Nevertheless, to address the gaps in our knowledge, it would be desirable to fund and establish prospective studies that continue active monitoring of patients as they transition from active disease treatment to palliative care. If possible, monitoring should be non-invasive as to not compromise patient comfort at the end of life. The great advances being made in patient monitoring with  wearable technologies might facilitate this and could be used for earlier detection of infections enabling quicker intervention. Caregiver involvement in reporting of symptoms may also play a role. Furthermore, consent to obtain more detailed information from the community and palliative care teams on the contributing factors to death would provide further insight. Patient and public engagement in this type of research will be critical, with studies indicating patient desire to participate in the right context 135 , 136 . In addition to information gathered before death, research autopsies have the potential to shed further light on the aetiology of death, such as thromboembolic events that may not have been detected in the absence of symptoms or diagnostic testing — discussed in Box  2 . Moreover, the availability of post-mortem samples can aid research into the biological underpinnings of metastases and processes leading to death. The greatest amount of information would be gained from cohorts additionally enrolled into warm autopsy programmes (Box  2 ).

Box 2 Research Autopsy Programmes and their optimization

Research autopsies are initiatives that involve the prompt collection of tissues from deceased individuals shortly after death, whereas tissue morphology is intact, and cells and tissues have not undergone substantial post-mortem changes. Research autopsy studies can be labour-intensive, and care is required in their logistical planning. The post-mortem interval (PMI) to autopsy can vary depending on the infrastructure available and can have implications for the utility of samples collected after death. For example, shorter PMIs achieved in rapid warm autopsy studies can more effectively facilitate in vitro (for example, cell line) and in vivo (for example, organoid and xenograft) models and can derive better quality RNA 157 , 158 . However, such studies are not easily established in the absence of out-of-hours facilities and expert input. Autopsies performed with longer PMIs, for example, up to several days after death, have been shown to have maintained tissue morphology and adequate DNA and RNA to facilitate cellular imaging techniques and genomic sequencing approaches 159 , 160 . Therefore, there is merit and general scientific value with autopsies regardless of the PMI, provided that consideration is given to the question being addressed, and the experimental approach.

The most powerful data are obtained from patients already involved in clinical studies before death. Information about disease course, longitudinal scans and tissue and blood analysis (cell counts, electrolytes, cytokines, metabolites and possibly circulating tumour DNA (ctDNA)) greatly enhances what can be learnt from post-mortem tissues. However, sensitivity is required to align the desire to acquire data with the wishes of the patients and their families, such that ultimately each autopsy has the potential to be meaningful and shed light on the biological processes leading to death.

More detailed observational clinical studies

Disease burden is not well correlated with survival; however, we propose that the accurate identification of prognostic factors correlating with survival should provide important insights into what ultimately precipitates mortality. As the cost of both targeted and non-targeted analyses of proteins and metabolites decreases, it should also become more feasible to explore molecular predictors of survival. Once identified, such factors could then be monitored in a targeted way prospectively with the potential to intervene where possible. In this setting, both the tumour and patient trajectory would receive precision-tailored treatments, the impact of which would need to be studied in randomized controlled trials. Even in the context of early phase trials, additional data could be obtained about patient symptoms in addition to safety considerations and tumour burden. Clinical imaging could also be exploited. Many patients receive computed tomography (CT) and positron-emission tomography (PET) scans, which contain abundant information about the burden and location of metastases and offer the opportunity to study changes in extent of adipose and muscle tissue and therefore body composition in relation to cachexia. Machine learning and artificial intelligence can be capitalized on to accurately measure these parameters, meaning that what would have previously been prohibitive owing to hours of radiologist time being required is now feasible 137 , 138 . In addition to the analysis of scans, the application of machine-learning approaches to metabolite, cytokine, immune cell and wearable technology-derived multimodal and multidimensional data may also uncover previously unknown parameters that correlate with mortality 139 . As outlined in Box  1 , incorporating psychosocial metrics into the study of late-stage cancer could also enable improvements in mental well-being of patients.

Increasing the relevance of model systems

Preclinical models will also have a place in determining the linkage between events found to precede death and cause of death; however, there should be an emphasis on reverse translation of questions from human studies to preclinical models. By way of example, this could involve modelling how metastases impinge on the ability of the body to respond to infection by challenging metastatic mouse models with a pathogen. Animal ethics and husbandry considerations mean that mice are housed in controlled environments in which exposure to pathogens is rare and the types of pathogen exposure are very narrow, so this type of information is currently lacking. To be optimally informative, practical and ethical complications around studying end-of-life physiology seen in patients need to be considered. Most models are chosen for their rapid progression, often with less than a month between primary or metastatic tumour seeding and death. These are not optimal for studying longer timescale chronic changes in patients. The development of slower progressing models, implementation of multiple lines of treatment and mimicking presence of other comorbidities should enable models to more accurately recapitulate observations made in patients. Furthermore, most preclinical cancer research currently uses young mice that fail to accurately mirror the interplay between ageing and cancer seen in humans 140 , with differences between chronological and immunological age providing a further confounding factor 141 . Researchers need to recognize the importance of and adopt more age-appropriate mouse models to better understand cancer mortality. In addition, most studies focus solely on tumour burden (which may only be possible at the point of death rather than longitudinally) or tumour size as a marker of disease, owing to the technical challenges of accurately quantifying organ impairment. Furthermore, tumour volume response and progression are poor surrogates of mortality in patients 142 ; therefore, better modelling of other metrics of tumour activity and impact on the body system may lead to better drug development. Minimizing and alleviating suffering in experimental animals is critical; hence ethical considerations limit the ability to study mortality in mice. Therefore, an expanded repertoire of analysis would help to understand how metastases impact specific systems and events, including the haematopoietic and nervous systems, as well as whole-body physiology and metabolism. Analysis of small volumes of blood can provide data on metabolites and cytokines, as well as complete blood counts (red blood cells, white blood cells and platelets), whereas increasingly sophisticated and automated technology is available to monitor mouse behaviour 143 . It is worth noting that weight loss is frequently used as a humane end point, which indicates that many cancer models trigger cachexia and that with appropriate measurements there is an opportunity to learn more about this phenomenon in existing models. We advocate more detailed reporting of why mice were culled in experimental studies — for example, tumour volume, weight loss, laboured breathing, complete blood cell counts and blood chemistry.

Clinical trials

The types of analyses detailed earlier will provide correlation between different factors and mortality, but not causative linkage. Ultimately, this information depends on testing in the context of clinical trials. Many of the mediators of immune dysfunction and cachexia can now be targeted with function blocking antibodies or forms of receptor traps and are being actively explored in clinical trials 115 , 144 . Several of these interventions were originally developed for chronic inflammatory conditions, which further highlights links between cancer and inflammation. The use of appropriately chosen secondary end points would provide an opportunity for testing whether correlative associations have a causal basis. In addition, many cancer drug trials stop providing an intervention at the point where a cancer progresses. The mechanisms behind cancer cachexia suggest that trials should be adapted to additionally consider clinical benefit in terms of weight, muscle loss and other specific determinants of efficacy, rather than solely monitor cancer progression.

Concluding remarks

Although efforts at cancer prevention and the development of curative treatment rightly receive considerable attention, we argue that understanding the precise events leading to cancer mortality should not be overlooked by funding bodies. Understanding the causes of dysfunction across multiple organ systems may provide novel strategies to manage symptoms of advanced cancer. Furthermore, better knowledge of the processes leading to death could enable patients and those around them to have essential discussions about their wishes and preferences, minimizing potentially inappropriate treatments and maximizing quality and enjoyment of life. In addition, more precise biomarkers of the likely timing of death may enable patients and their families to better utilize the time that is left. In the longer term, strategies to prevent organ dysfunction should offer considerable benefits to both patients with high tumour burden and those who have low disease burden but die from factors produced by cancer.

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Acknowledgements

A.B. is funded by National Institutes of Health/National Cancer Institute P30 CA008748 and R01-CA245499. K.B. is employed by the UK National Health Service. T.R.C. acknowledges funding support from the National Health and Medical Research Council (NHMRC) Ideas (2000937), Project (1129766, 1140125), Development (2013881) and Fellowship (1158590) schemes, a Cancer Institute NSW Career Development Fellowship (CDF171105), Cancer Council NSW project support (RG19-09, RG23-11) and Susan G. Komen for the Cure (CCR17483294). T.G. is funded by the Cancer Prevention and Research Institute of Texas Grant 00011633. M.J.-H. has received funding from CRUK, NIH National Cancer Institute, IASLC International Lung Cancer Foundation, Lung Cancer Research Foundation, Rosetrees Trust, UKI NETs and NIHR. T.J. acknowledges funding from Cancer Grand Challenges (NIH: 1OT2CA278690-01; CRUK: CGCATF-2021/100019), the Mark Foundation for Cancer Research (20-028-EDV), the Osprey Foundation, Fortune Footwear, Cold Spring Harbour Laboratory (CSHL) and developmental funds from CSHL Cancer Center Support Grant 5P30CA045508. R.K. is funded by the Intramural Research Program, the National Cancer Institute, NIH Clinical Center and the National Institutes of Health (NIH NCI ZIABC011332-06 and NIH NCI ZIABC011334-10). R.L. is supported by a Wellcome Early Career Investigator Award (225724/Z/22/Z). E.S. is supported by the Francis Crick Institute, which receives its core funding from Cancer Research UK (CC2040), the UK Medical Research Council (CC2040) and the Wellcome Trust (CC2040) and the European Research Council (ERC Advanced Grant CAN_ORGANISE, Grant agreement number 101019366). E.S. reports personal grants from Mark Foundation and the European Research Council. C.S. is a Royal Society Napier Research Professor (RSRP\R\210001). His work is supported by the Francis Crick Institute that receives its core funding from Cancer Research UK (CC2041), the UK Medical Research Council (CC2041) and the Wellcome Trust (CC2041) and the European Research Council under the European Union’s Horizon 2020 research and innovation programme (ERC Advanced Grant PROTEUS Grant agreement number 835297). M.G.V.H. reports support from the Lustgarten Foundation, the MIT Center for Precision Cancer Medicine, the Ludwig Center at MIT and NIH grants R35 CA242379 and P30 CA1405141.

Author information

These authors contributed equally: Adrienne Boire, Katy Burke, Thomas R. Cox, Theresa Guise, Mariam Jamal-Hanjani, Tobias Janowitz, Rosandra Kaplan, Rebecca Lee, Charles Swanton, Matthew G. Vander Heiden, Erik Sahai.

Authors and Affiliations

Memorial Sloan Kettering Cancer Center, New York, NY, USA

Adrienne Boire

University College London Hospitals NHS Foundation Trust and Central and North West London NHS Foundation Trust Palliative Care Team, London, UK

Cancer Ecosystems Program, The Garvan Institute of Medical Research and The Kinghorn Cancer Centre, Darlinghurst, New South Wales, Australia

Thomas R. Cox

School of Clinical Medicine, St Vincent’s Healthcare Clinical Campus, UNSW Medicine and Health, UNSW Sydney, Sydney, New South Wales, Australia

Department of Endocrine Neoplasia and Hormonal Disorders, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

Theresa Guise

Cancer Metastasis Laboratory, University College London Cancer Institute, London, UK

Mariam Jamal-Hanjani

Department of Oncology, University College London Hospitals, London, UK

Mariam Jamal-Hanjani & Charles Swanton

Cancer Research UK Lung Centre of Excellence, University College London Cancer Institute, London, UK

Cold Spring Harbour Laboratory, Cold Spring Harbour, New York, NY, USA

Tobias Janowitz

Northwell Health Cancer Institute, New York, NY, USA

Paediatric Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA

Rosandra Kaplan

Tumour Cell Biology Laboratory, The Francis Crick Institute, London, UK

Rebecca Lee & Erik Sahai

Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK

Rebecca Lee

Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK

Charles Swanton

Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA

Matthew G. Vander Heiden

Dana-Farber Cancer Institute, Boston, MA, USA

You can also search for this author in PubMed   Google Scholar

Contributions

All authors researched data for the article. A.B., K.B., T.R.C., T.G., T.J., C.S., M.G.V.H, R.K., M.J.-H. and E.S. contributed substantially to discussion of the content. T.C., R.L. and E.S. wrote the article. All authors reviewed and/or edited the manuscript before submission.

Corresponding authors

Correspondence to Thomas R. Cox or Erik Sahai .

Ethics declarations

Competing interests.

A.B. is an inventor on pending patents 63/449,817, 63/052,139 as well as awarded patents 11,305,014 and 10,413,522; all issued to the Sloan Kettering Institute. She has received personal fees from Apelis Pharmaceuticals and serves as an unpaid member of the Evren Technologies SAB. K.B., T.R.C., T.G., T.J. and R.K. declare no competing interests. M.J.-H. reports support from Achilles Therapeutics Scientific Advisory Board and Steering Committee, Pfizer, Astex Pharmaceuticals, Oslo Cancer Cluster and Bristol Myers Squibb outside the submitted work. R.L. reports personal fees from Pierre Fabre and has research funding from BMS, Astra Zeneca and Pierre Fabre outside the submitted work. E.S. reports grants from Novartis, Merck Sharp Dohme, AstraZeneca and personal fees from Phenomic outside the submitted work. C.S. reports grants and personal fees from Bristol Myers Squibb, AstraZeneca, Boehringer-Ingelheim, Roche-Ventana, personal fees from Pfizer, grants from Ono Pharmaceutical, Personalis, grants, personal fees and other support from GRAIL, other support from AstraZeneca and GRAIL, personal fees and other support from Achilles Therapeutics, Bicycle Therapeutics, personal fees from Genentech, Medixci, China Innovation Centre of Roche (CiCoR) formerly Roche Innovation Centre, Metabomed, Relay Therapeutics, Saga Diagnostics, Sarah Canon Research Institute, Amgen, GlaxoSmithKline, Illumina, MSD, Novartis, other support from Apogen Biotechnologies and Epic Bioscience outside the submitted work; in addition, C.S. has a patent for PCT/US2017/028013 licensed to Natera Inc., UCL Business, a patent for PCT/EP2016/059401 licensed to Cancer Research Technology, a patent for PCT/EP2016/071471 issued to Cancer Research Technology, a patent for PCT/GB2018/051912 pending, a patent for PCT/GB2018/052004 issued to Francis Crick Institute, University College London, Cancer Research Technology Ltd, a patent for PCT/GB2020/050221 issued to Francis Crick Institute, University College London, a patent for PCT/EP2022/077987 pending to Cancer Research Technology, a patent for PCT/GB2017/053289 licensed, a patent for PCT/EP2022/077987 pending to Francis Crick Institute, a patent for PCT/EP2023/059039 pending to Francis Crick Institute and a patent for PCT/GB2018/051892 pending to Francis Crick Institute. C.S. is Co-chief Investigator of the NHS Galleri trial funded by GRAIL. He is Chief Investigator for the AstraZeneca MeRmaiD I and II clinical trials and Chair of the Steering Committee. C.S. is cofounder of Achilles Therapeutics and holds stock options. M.G.V.H. is a scientific adviser for Agios Pharmaceuticals, iTeos Therapeutics, Sage Therapeutics, Faeth Therapeutics, Droia Ventures and Auron Therapeutics on topics unrelated to the presented work.

Peer review

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Nature Reviews Cancer thanks Vickie Baracos, Clare M. Isacke, who co-reviewed with Amanda Fitzpatrick and Erica Sloan and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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An autoimmune encephalitis characterized by complex neuropsychiatric features and the presence of immunoglobulin G (IgG) antibodies against the NR1 subunit of the NMDA receptors in the central nervous system.

Partial collapse or incomplete inflation of the lung.

Pressure-induced movement of brain tissue.

An ageing-associated process in which haematopoiesis becomes dominated by one or a small number of genetically distinct stem or progenitor cells. Clonal haematopoiesis is linked to an increased risk of haematological malignancies.

Inability of the heart to pump blood properly.

Constriction of the arteries that supply blood to the heart.

(CRH). One of the major factors that drives the response of the body to stress.

(DIC). A rare but serious condition in which abnormal blood clotting occurs throughout the blood vessels of the body.

Inflammation of the brain.

An abnormal connection that forms between two body parts, such as an organ or blood vessel and another often unrelated structure in close proximity.

A rare disorder in which the immune system of a body attacks the nerves, which can lead to paralysis.

The stopping of flow of blood, typically associated with the bodies response to prevent and stop bleeding.

A build-up of fluid within the cavities of the brain.

Elevated calcium levels in the blood, often caused by overactive parathyroid glands. Hypercalcaemia is linked to kidney stones, weakened bones, altered digestion and potentially altered cardiac and brain function.

(HPD). Rapid tumour progression sometimes observed during immune checkpoint inhibitor treatment.

The condition that occurs when the level of sodium in the blood is low.

Harm, which is often unavoidable, caused by cancer treatments.

The marked suppression of polyclonal immunoglobulins in the body.

(LEMS). A neuromuscular junction disorder affecting communication between nerves and muscles, which manifests as a result of a paraneoplastic syndrome or a primary autoimmune disorder. Many cases are associated with small-cell lung cancer.

When cancer cells spread to the tissue layers covering the brain and spinal cord (the leptomeninges).

Also known as pulmonary oedema is a condition caused by excess fluid in the lungs. This fluid collects in the alveoli compromising function and making it difficult to breathe.

The observation of displacement of brain tissue across the centre line of the brain, suggestive of uneven intracranial pressure.

Decreased blood flow to the myocardium, commonly called a heart attack.

Inflammation specifically of the middle layer of the heart wall.

A group of rare disorders that occur when the immune system reacts to changes in the body triggered by the presence of a neoplasm.

A dense network of nerves that transmit information from the brain (efferent neurons) to the periphery and conversely transmit information from the periphery to the brain (afferent neurons). A component of the peripheral nervous system is the autonomic nervous system.

A build-up of fluid between the tissues that line the lungs and the chest wall.

A condition characterized by loss of skeletal muscle mass and function.

The lodging of a circulating blood clot within a vessel leading to obstruction. Thromboembolisms may occur in veins (venous thromboembolism) and arteries (arterial thromboembolism).

A key component of the pathway regulating blood clotting, specifically the receptor and cofactor for factor VII/VIIa.

A syndrome occurs when tumour cells release their contents into the bloodstream, either spontaneously or more typically, in response to therapeutic intervention.

Devices worn on the body, typically in the form of accessories or clothing, that incorporate advanced electronics and technology to monitor, track or enhance various aspects of human life. Examples include smartwatches and fitness trackers.

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Boire, A., Burke, K., Cox, T.R. et al. Why do patients with cancer die?. Nat Rev Cancer 24 , 578–589 (2024). https://doi.org/10.1038/s41568-024-00708-4

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Published : 19 June 2024

Issue Date : August 2024

DOI : https://doi.org/10.1038/s41568-024-00708-4

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How to Write a Research Paper [Steps & Examples]

As a student, you are often required to complete numerous academic tasks, which can demand a lot of extra effort. Writing a research paper is one of these tasks. If researching for the topic isn't challenging enough, writing it down in a specific format adds another layer of difficulty. Having gone through this myself, I want to help you have a smoother journey in writing your research paper. I'll guide you through everything you need to know about writing a research paper, including how to write a research paper and all the necessary factors you need to consider while writing one.

Order for Preparation of your research paper

Before beginning your research paper, start planning how you will organize your paper. Follow the specific order I have laid out to ensure you assemble everything correctly, cover all necessary components, and write more effectively. This method will help you avoid missing important elements and improve the overall quality of your paper.

Figures and Tables

Assemble all necessary visual aids to support your data and findings. Ensure they are labeled correctly and referenced appropriately in your text.

Detail the procedures and techniques used in your research. This section should be thorough enough to allow others to replicate your study.

Summarize the findings of your research without interpretation. Use figures and tables to illustrate your data clearly.

Interpret the results, discussing their implications and how they relate to your research question. Address any limitations and suggest areas for future research.

Summarize the key points of your research, restating the significance of your findings and their broader impact.

Introduction

Introduce the topic, provide background information, and state the research problem or hypothesis. Explain the purpose and scope of your study.

Write a concise summary of your research, including the objective, methods, results, and conclusion. Keep it brief and to the point.

Create a clear and informative title that accurately reflects the content and focus of your research paper.

Identify key terms related to your research that will help others find your paper in searches.

Acknowledgements

Thank those who contributed to your research, including funding sources, advisors, and any other significant supporters.

Compile a complete list of all sources cited in your paper, formatted according to the required citation style. Ensure every reference is accurate and complete.

Types of Research Papers

There are multiple types of research papers, each with distinct characteristics, purposes, and structures. Knowing which type of research paper is required for your assignment is crucial, as each demands different preparation and writing strategies. Here, we will delve into three prominent types: argumentative, analytical, and compare and contrast papers. We will discuss their characteristics, suitability, and provide detailed examples to illustrate their application.

A.Argumentative Papers

Characteristics:

An argumentative or persuasive paper is designed to present a balanced view of a controversial issue, but ultimately aims to persuade the reader to adopt the writer's perspective. The key characteristics of this type of paper include:

Purpose: The primary goal is to convince the reader to support a particular stance on an issue. This is achieved by presenting arguments, evidence, and refuting opposing viewpoints.

Structure: Typically structured into an introduction, a presentation of both sides of the issue, a refutation of the opposing arguments, and a conclusion that reinforces the writer’s position.

Tone: While the tone should be logical and factual, it should not be overly emotional. Arguments must be supported with solid evidence, such as statistics, expert opinions, and factual data.

Suitability:

Argumentative papers are suitable for topics that have clear, opposing viewpoints. They are often used in debates, policy discussions, and essays aimed at influencing public opinion or academic discourse.

Topic: "Should governments implement universal basic income?"

Pro Side: Universal basic income provides financial security, reduces poverty, and can lead to a more equitable society.

Con Side: It could discourage work, lead to higher government expenditure, and might not be a sustainable long-term solution.

Argument: After presenting both sides, the paper would argue that the benefits of reducing poverty and financial insecurity outweigh the potential drawbacks, using evidence from various studies and real-world examples.

Writing Tips:

Clearly articulate your position on the issue from the beginning.

Present balanced arguments by including credible sources that support both sides.

Refute counterarguments effectively with logical reasoning and evidence.

Maintain a factual and logical tone, avoiding excessive emotional appeals.

B.Analytical Papers

An analytical research paper is focused on breaking down a topic into its core components, examining various perspectives, and drawing conclusions based on this analysis. The main characteristics include:

Purpose: To pose a research question, collect data from various sources, analyze different viewpoints, and synthesize the information to arrive at a personal conclusion.

Structure: Includes an introduction with a clear research question, a literature review that summarizes existing research, a detailed analysis, and a conclusion that summarizes findings.

Tone: Objective and neutral, avoiding personal bias or opinion. The focus is on data and logical analysis.

Analytical research papers are ideal for topics that require detailed examination and evaluation of various aspects. They are common in disciplines such as social sciences, humanities, and natural sciences, where deep analysis of existing research is crucial.

Topic: "The impact of social media on mental health."

Research Question: How does social media usage affect mental well-being among teenagers?

Analysis: Examine studies that show both positive (e.g., social support) and negative (e.g., anxiety and depression) impacts of social media. Analyze the methodologies and findings of these studies.

Conclusion: Based on the analysis, conclude whether the overall impact is more beneficial or harmful, remaining neutral and presenting evidence without personal bias.

Maintain an objective and neutral tone throughout the paper.

Synthesize information from multiple sources, ensuring a comprehensive analysis.

Develop a clear thesis based on the findings from your analysis.

Avoid inserting personal opinions or biases.

C.Compare and Contrast Papers

Compare and contrast papers are used to analyze the similarities and differences between two or more subjects. The key characteristics include:

Purpose: To identify and examine the similarities and differences between two or more subjects, providing a comprehensive understanding of their relationship.

Structure: Can be organized in two ways:

Point-by-Point: Each paragraph covers a specific point of comparison or contrast.

Subject-by-Subject: Each subject is discussed separately, followed by a comparison or contrast.

Tone: Informative and balanced, aiming to provide a thorough and unbiased comparison.

Compare and contrast papers are suitable for topics where it is important to understand the distinctions and similarities between elements. They are commonly used in literature, history, and various comparative studies.

Topic: "Compare and contrast the leadership styles of Martin Luther King Jr. and Malcolm X."

Comparison Points: Philosophies (non-violence vs. militant activism), methods (peaceful protests vs. more radical approaches), and impacts on the Civil Rights Movement.

Analysis: Describe each leader's philosophy and method, then analyze how these influenced their effectiveness and legacy.

Conclusion: Summarize the key similarities and differences, and discuss how both leaders contributed uniquely to the movement.

Provide equal and balanced coverage to each subject.

Use clear criteria for comparison, ensuring logical and coherent analysis.

Highlight both similarities and differences, ensuring a nuanced understanding of the subjects.

Maintain an informative tone, focusing on objective analysis rather than personal preference.

How to Write A Research Paper [Higher Efficiency & Better Results]

Conduct Preliminary Research

Before we get started with the research, it's important to gather relevant information related to it. This process, also known as the primary research method, helps researchers gain preliminary knowledge about the topic and identify research gaps. Whenever I begin researching a topic, I usually utilize Google and Google Scholar. Another excellent resource for conducting primary research is campus libraries, as they provide a wealth of great articles that can assist with your research.

Now, let's see how WPS Office and AIPal can be great research partners:

Let's say that I have some PDFs which I have gathered from different sources. With WPS Office, these PDFs can be directly uploaded not just to extract key points but also to interact with the PDF with special help from WPS AI.

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Step 3: This will open the WPS PDF AI pane on the right side of the screen. Click on "Upload".

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Step 5: To interact further with the document, click on the "Inquiry" tab to talk with WPS AI and get more information on the contents of the PDF.

Research is incomplete without a Google search, but what exactly should you search for? AIPal can help you with these answers. AIPal is a Chrome extension that can help researchers make their Google searches and interactions with Chrome more effective and efficient. If you haven't installed AIPal on Chrome yet, go ahead and download the extension; it's completely free to use:

Step 1: Let's search for a term on Google related to our research.

Step 2: An AIPal widget will appear right next to the Google search bar, click on it.

Step 3: Upon clicking it, an AIPal window will pop up. In this window, you will find a more refined answer for your searched term, along with links most relevant to your search, providing a more refined search experience.

WPS AI can also be used to extract more information with the help of WPS Writer.

Step 1: We might have some information saved in a Word document, either from lectures or during preliminary research. We can use WPS AI within Writer to gain more insights.

Step 2: Select the entire text you want to summarize or understand better.

Step 3: Once the text is selected, a hover menu will appear. Click on the "WPS AI" icon in this menu.

Step 4: From the list of options, click on "Explain" to understand the content more deeply, or click on "Summarize" to shorten the paragraph.

Step 5: The results will be displayed in a small WPS AI window.

Develop the Thesis statement

To develop a strong thesis statement, start by formulating a central question your paper will address. For example, if your topic is about the impact of social media on mental health, your thesis statement might be:

"Social media use has a detrimental effect on mental health by increasing anxiety, depression, and loneliness among teenagers."

This statement is concise, contentious, and sets the stage for your research. With WPS AI, you can use the "Improve" feature to refine your thesis statement, ensuring it is clear, coherent, and impactful.

Write the First draft

Begin your first draft by focusing on maintaining forward momentum and clearly organizing your thoughts. Follow your outline as a guide, but be flexible if new ideas emerge. Here's a brief outline to get you started:

Using WPS AI’s "Make Longer" feature, you can quickly elaborate key ideas and points of your studies and articles into a descriptive format to include in your draft, saving time and ensuring clarity.

Compose Introduction, Body and Conclusion paragraphs

When writing a research paper, it’s essential to transform your key points into detailed, descriptive paragraphs. WPS AI can help you streamline this process by enhancing your key points, ensuring each section of your paper is well-developed and coherent. Here’s how you can use WPS AI to compose your introduction, body, and conclusion paragraphs:

Let's return to the draft and start composing our introduction. The introduction should provide the background of the research paper and introduce readers to what the research paper will explore.

If your introduction feels too brief or lacks depth, use WPS AI’s "Make Longer" feature to expand on key points, adding necessary details and enhancing the overall narrative.

Once the introduction is completed, the next step is to start writing the body paragraphs and the conclusion of our research paper. Remember, the body paragraphs will incorporate everything about your research: methodologies, challenges, results, and takeaways.

If this paragraph is too lengthy or repetitive, WPS AI’s "Make Shorter" feature can help you condense it without losing essential information.

Write the Second Draft

In the second draft, refine your arguments, ensure logical flow, and check for clarity. Focus on eliminating any unnecessary information, ensuring each paragraph supports your thesis statement, and improving transitions between ideas. Incorporate feedback from peers or advisors, and ensure all citations are accurate and properly formatted. The second draft should be more polished and coherent, presenting your research in a clear and compelling manner.

WPS AI’s "Improve Writing" feature can be particularly useful here to enhance the overall quality and readability of your paper.

WPS Spellcheck can assist you in correcting spelling and grammatical errors, ensuring your paper is polished and professional. This tool helps you avoid common mistakes and enhances the readability of your paper, making a significant difference in the overall quality.

Bonus Tips: How to Get Inspiration for your Research Paper- WPS AI

WPS Office is a phenomenal office suite that students find to be a major blessing. Not only is it a free office suite equipped with advanced features that make it competitive in the market, but it also includes a powerful AI that automates and enhances many tasks, including writing a research paper. In addition to improving readability with its AI Proofreader tool, WPS AI offers two features, "Insight" and "Inquiry", that can help you gather information and inspiration for your research paper:

Insight Feature:

The Insight feature provides deep insights and information on various topics and fields. It analyzes literature to extract key viewpoints, trends, and research directions. For instance, if you're writing a research paper on the impact of social media on mental health, you can use the Insight feature to gather a comprehensive overview of the latest studies, key arguments, and emerging trends in this field. This helps you build a solid foundation for your paper and ensure you are covering all relevant aspects.

Inquiry Feature:

The Inquiry feature allows you to ask specific questions related to your research topic. This helps you gather necessary background information and refine your research focus effectively. For example, if you need detailed information on how social media usage affects teenagers' self-esteem, you can use the Inquiry feature to ask targeted questions and receive relevant answers based on the latest research.

FAQs about writing a research paper

1. can any source be used for academic research.

No, it's essential to use credible and relevant sources. Here is why:

Developing a Strong Argument: Your research paper relies on evidence to substantiate its claims. Using unreliable sources can undermine your argument and harm the credibility of your paper.

Avoiding Inaccurate Information: The internet is abundant with data, but not all sources can be considered reliable. Credible sources guarantee accuracy.

2. How can I avoid plagiarism?

To avoid plagiarism, follow these steps:

Keep Records of Your Sources: Maintain a record of all the sources you use while researching. This helps you remember where you found specific ideas or phrases and ensures proper attribution.

Quote and Paraphrase Correctly: When writing a paper, use quotation marks for exact words from a source and cite them properly. When paraphrasing, restate the idea in your own words and include a citation to acknowledge the original source.

Utilize a Plagiarism Checker: Use a plagiarism detection tool before submitting your paper. This will help identify unintentional plagiarism, ensuring your paper is original and properly referenced.

3. How can I cite sources properly?

Adhere to the citation style guide (e.g., APA, MLA) specified by your instructor or journal. Properly citing all sources both within the text and in the bibliography or references section is essential for maintaining academic integrity and providing clear credit to the original authors. This practice also helps readers locate and verify the sources you've used in your research.

4. How long should a research paper be?

The length of a research paper depends on its topic and specific requirements. Generally, research papers vary between 4,000 to 6,000 words, with shorter papers around 2,000 words and longer ones exceeding 10,000 words. Adhering to the length requirements provided for academic assignments is essential. More intricate subjects or extensive research often require more thorough explanations, which can impact the overall length of the paper.

Write Your Research Paper with the Comfort of Using WPS Office

Writing a research paper involves managing numerous complicated tasks, such as ensuring the correct formatting, not missing any crucial information, and having all your data ready. The process of how to write a research paper is inherently challenging. However, if you are a student using WPS Office, the task becomes significantly simpler. WPS Office, especially with the introduction of WPS AI, provides all the resources you need to write the perfect research paper. Download WPS Office today and discover how it can transform your research paper writing experience for the better.

• 1. How to Write a Proposal [ Steps & Examples]
• 2. Free Graph Paper: Easy Steps to Make Printable Graph Paper PDF
• 3. How to Write an Abstract - Steps with Examples
• 4. How to Write a Conclusion - Steps with Examples
• 5. How to Use WPS AI/Chatgpt to Write Research Papers: Guide for Beginners
• 6. How to Write a Hook- Steps With Examples

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New climate and sustainability research efforts will focus on eight ‘Solution Areas’

The Stanford Doerr School of Sustainability will establish new research initiatives under topics including climate, water, energy, food, nature, and cities.

The Stanford Doerr School of Sustainability has selected eight interconnected Solution Areas to focus its research efforts over the next decade. This new research plan amplifies the school’s ability to translate Stanford research into large-scale solutions and inform key decision makers in policy and business.

Selected based on extensive faculty input and assessment of where Stanford can make the most meaningful impact, the eight areas are: climate; water; energy; food; risk, resilience, and adaptation; nature; cities; and platforms and tools for monitoring and decision making. 

“Solution Areas identify and leverage the critical junctions between the most pressing global sustainability challenges and the areas where Stanford has the talent and expertise to find solutions,” said Dean Arun Majumdar. “This collaborative all-campus approach expands and strengthens our commitment to using all the power we have – the knowledge, the education, the talent, the innovation, the resources, the influence – to build a thriving planet for future generations.” 

‘Integrative Projects’ and ‘Flagship Destinations’

In each Solution Area, the school plans to build two types of research initiatives. One type, called Integrative Projects, will be managed by the school’s institutes, including the Stanford Woods Institute for the Environment , the Precourt Institute for Energy , and a planned Sustainable Societies Institute. 

Integrative Projects will be organized around decade-long research themes and dedicated to creating solutions through interdisciplinary collaboration, engagement with partners beyond Stanford, identifying significant knowledge gaps, and understanding systems.

According to Chris Field , the Perry L. McCarty Director of the Stanford Woods Institute for the Environment and a professor in the Stanford Doerr School of Sustainability and the School of Humanities and Sciences , the new commitment to these areas “will provide both resources and coordination that expand Stanford faculty’s capacity to deliver sustainability solutions at scale.” 

A second type of research initiative, called Flagship Destinations, is managed by Stanford’s Sustainability Accelerator . Flagship Destinations are targets for the pace and scale of work to address challenges facing Earth, climate, and society. For example, the school’s first Flagship Destination, announced in 2023 , calls for enabling the removal of billions of tons of planet-warming gases annually from Earth’s atmosphere by the middle of this century. By working backward from sustainability targets in consultation with faculty and external experts, this initiative seeks to rapidly translate Stanford research into policy and technology solutions. Additional Flagship Destinations will be announced later this week.

Whereas Integrative Projects are designed to produce knowledge and evidence that can eventually lead to solutions, Flagship Destination projects are intended to help verify and demonstrate that well-studied solutions can succeed at large scale so they can be launched out of Stanford and implemented for the benefit of humanity and our planet. Scalable solutions nurtured and launched through these projects could take the form of policy frameworks, open-source platforms, nonprofit organizations, new for-profit companies, and ongoing collaborations all committed to addressing pressing sustainability challenges.

“By working together in these Solution Areas across disciplines and with collaborators beyond the university, we maximize our ability to have positive impacts on the timeframe and scale needed for the planet and humanity,” said Scott Fendorf , senior associate dean for integrative initiatives and the Terry Huffington Professor in the Stanford Doerr School of Sustainability. 

Workshops will be held with faculty and external experts to develop research strategies for each Solution Area on a rolling basis. Strategy workshops, opportunities to provide input on future Integrative Projects, and requests for proposals (open to all Stanford faculty) will be announced in the coming months.

Related message from leadership: Read a letter to faculty about the new Solution Areas from Dean Majumdar with Precourt Institute for Energy director William Chueh; Stanford Woods Institute for the Environment director Chris Field; Accelerator faculty director Yi Cui and executive director Charlotte Pera; and Integrative Initiatives associate dean Jenna Davis and senior associate dean Scott Fendorf.

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Stanford’s Sustainability Accelerator adds new targets

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Title: the ai scientist: towards fully automated open-ended scientific discovery.

Abstract: One of the grand challenges of artificial general intelligence is developing agents capable of conducting scientific research and discovering new knowledge. While frontier models have already been used as aides to human scientists, e.g. for brainstorming ideas, writing code, or prediction tasks, they still conduct only a small part of the scientific process. This paper presents the first comprehensive framework for fully automatic scientific discovery, enabling frontier large language models to perform research independently and communicate their findings. We introduce The AI Scientist, which generates novel research ideas, writes code, executes experiments, visualizes results, describes its findings by writing a full scientific paper, and then runs a simulated review process for evaluation. In principle, this process can be repeated to iteratively develop ideas in an open-ended fashion, acting like the human scientific community. We demonstrate its versatility by applying it to three distinct subfields of machine learning: diffusion modeling, transformer-based language modeling, and learning dynamics. Each idea is implemented and developed into a full paper at a cost of less than $15 per paper. To evaluate the generated papers, we design and validate an automated reviewer, which we show achieves near-human performance in evaluating paper scores. The AI Scientist can produce papers that exceed the acceptance threshold at a top machine learning conference as judged by our automated reviewer. This approach signifies the beginning of a new era in scientific discovery in machine learning: bringing the transformative benefits of AI agents to the entire research process of AI itself, and taking us closer to a world where endless affordable creativity and innovation can be unleashed on the world's most challenging problems. Our code is open-sourced at this https URL

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New Book Provides Strategies for ‘Being Present’ in the Age of Digital Distraction

Maria Camila Gutierrez

January 10, 2022

Technology has streamlined the way we work, live, and interact with those around us. People are able to connect from anywhere in the world, at any time. But what happens when we allow these devices to hinder us more than they help us? 

In her new book, Being Present , Jeanine W. Turner, professor at Georgetown University who teaches in the Communication, Culture, and Technology Program and in the McDonough School of Business, provides strategies for paying attention and commanding attention in a time of digital distraction. 

Being Present highlights the strains placed on individuals to navigate the complexities of social presence when competing against smartphones, computers, work applications, and numerous other digital devices – both in their work and home settings. These challenges have been further intensified during the pandemic as homes suddenly accommodated both work and family life. 

“It is increasingly difficult to break through the digital noise to earn someone’s undivided attention, engage in meaningful interactions, and balance when to be “online” and “offline” in today’s virtual environment,” said Turner. “I wrote this book for anyone that feels frustrated when trying to communicate more effectively in a digital world, and I hope it helps readers approach their social presence with thoughtfulness in all areas of their life.”

Drawing from 15 years of research, interviews, and experience from teaching students and executives, Turner blends original research and theoretical application to introduce a new framework for social presence. She outlines four primary communication choices – budgeted, entitled, competitive, and invitational – and guides readers on when and where to employ each strategy to effectively allocate attention as a listener and how to maintain attention from a distracted audience. Each chapter includes concrete strategies and concludes with reflection questions and exercises to help readers further explore these decisions in professional and personal relationships.

“Being Present is a highly engaging and insightful guide for anyone hoping to communicate more effectively,” said Suzanne Clark, president and CEO of the U.S. Chamber of Commerce. “As new technology continues to affect the ways we interact, this book provides actionable strategies to cut through the noise and actively cultivate a social presence. I would recommend it to anyone hoping to be a more effective leader.” 

To celebrate the launch of Turner’s book, the McDonough School of Business, Georgetown University Press, and the Communication, Culture, and Technology Program invite students, alumni, community members, faculty, and staff to a virtual book launch on February 10, 2022, at 12:00 p.m. Participants will have the opportunity to hear directly from the author and engage in Q&A about the book. 

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Research on a low-carbon optimization strategy for regional power grids considering a dual demand response of electricity and carbon.

1. Introduction

2. obtaining dynamic carbon emission factors, 2.1. introduction of power flow tracking, 2.1.1. the principle of complex proportional sharing, 2.1.2. power supply path analysis, 2.1.3. equivalent treatment of line loss, 2.2. power flow tracking method based on complex power, 2.3. carbon flow tracking model, 3. source-load cooperative multi-objective bi-level model framework, 3.1. low-carbon demand response mechanism, 3.2. carbon emission accounting of flexible loads based on the dynamic carbon emission factor, 3.3. source-load cooperative multi-objective bi-level model framework, 4. source-load collaborative multi-objective bi-level optimization model, 4.1. upper model: multi-objective low-carbon economic dispatch model of power grid operator, 4.1.1. objective function, 4.1.2. constraint condition, 4.2. lower level model: economic dispatch model of load aggregator under electricity–carbon dual demand response, 4.3. model solving, 4.3.1. non-dominated sorting genetic algorithm-ii, 4.3.2. fuzzy satisfaction decision-making based on the logistic membership function, 4.3.3. model solving process, 5. case analysis, 5.1. parameter settings, 5.2. upper model optimization results analysis, 5.2.1. pareto optimal set analysis, 5.2.2. influence of wind turbine access location on dispatching results, 5.3. multi-objective bi-level model optimization results, 5.3.1. multi-objective bi-level model scheduling results, 5.3.2. optimal scheduling results considering regional carbon emission balance, 5.3.3. the influence of different reference membership on fuzzy optimization results, 5.3.4. comparative analysis of the situation before and after load scheduling, 5.3.5. dynamic carbon emission factor distribution results, 5.3.6. comparative analysis of regional carbon emission factors on the power generation side and demand side, 6. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

• Kang, C.Q.; Du, E.S.; Guo, H.Y.; Li, Y.W.; Fang, Y.C.; Zhang, N.; Zhong, H.W. Primary exploration of six essential factors in new power system. Power Syst. Technol. 2023 , 47 , 1741–1750. [ Google Scholar ]
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Ma, F.; Ying, L.; Cui, X.; Yu, Q. Research on a Low-Carbon Optimization Strategy for Regional Power Grids Considering a Dual Demand Response of Electricity and Carbon. Sustainability 2024 , 16 , 7000. https://doi.org/10.3390/su16167000

Ma F, Ying L, Cui X, Yu Q. Research on a Low-Carbon Optimization Strategy for Regional Power Grids Considering a Dual Demand Response of Electricity and Carbon. Sustainability . 2024; 16(16):7000. https://doi.org/10.3390/su16167000

Ma, Famei, Liming Ying, Xue Cui, and Qiang Yu. 2024. "Research on a Low-Carbon Optimization Strategy for Regional Power Grids Considering a Dual Demand Response of Electricity and Carbon" Sustainability 16, no. 16: 7000. https://doi.org/10.3390/su16167000

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How to Create a Structured Research Paper Outline | Example

Published on August 7, 2022 by Courtney Gahan . Revised on August 15, 2023.

A research paper outline is a useful tool to aid in the writing process , providing a structure to follow with all information to be included in the paper clearly organized.

A quality outline can make writing your research paper more efficient by helping to:

• Organize your thoughts
• Understand the flow of information and how ideas are related
• Ensure nothing is forgotten

A research paper outline can also give your teacher an early idea of the final product.

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Table of contents

Research paper outline example, how to write a research paper outline, formatting your research paper outline, language in research paper outlines.

• Definition of measles
• Rise in cases in recent years in places the disease was previously eliminated or had very low rates of infection
• Figures: Number of cases per year on average, number in recent years. Relate to immunization
• Symptoms and timeframes of disease
• Risk of fatality, including statistics
• How measles is spread
• Immunization procedures in different regions
• Different regions, focusing on the arguments from those against immunization
• Immunization figures in affected regions
• High number of cases in non-immunizing regions
• Illnesses that can result from measles virus
• Fatal cases of other illnesses after patient contracted measles
• Summary of arguments of different groups
• Summary of figures and relationship with recent immunization debate
• Which side of the argument appears to be correct?

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The AI-powered Citation Checker helps you avoid common mistakes such as:

• Missing commas and periods
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Follow these steps to start your research paper outline:

• Decide on the subject of the paper
• Write down all the ideas you want to include or discuss
• Organize related ideas into sub-groups
• Arrange your ideas into a hierarchy: What should the reader learn first? What is most important? Which idea will help end your paper most effectively?
• Create headings and subheadings that are effective
• Format the outline in either alphanumeric, full-sentence or decimal format

There are three different kinds of research paper outline: alphanumeric, full-sentence and decimal outlines. The differences relate to formatting and style of writing.

• Alphanumeric
• Full-sentence

An alphanumeric outline is most commonly used. It uses Roman numerals, capitalized letters, arabic numerals, lowercase letters to organize the flow of information. Text is written with short notes rather than full sentences.

• Sub-point of sub-point 1

Essentially the same as the alphanumeric outline, but with the text written in full sentences rather than short points.

• Additional sub-point to conclude discussion of point of evidence introduced in point A

A decimal outline is similar in format to the alphanumeric outline, but with a different numbering system: 1, 1.1, 1.2, etc. Text is written as short notes rather than full sentences.

• 1.1.1 Sub-point of first point
• 1.1.2 Sub-point of first point
• 1.2 Second point

To write an effective research paper outline, it is important to pay attention to language. This is especially important if it is one you will show to your teacher or be assessed on.

There are four main considerations: parallelism, coordination, subordination and division.

Parallelism: Be consistent with grammatical form

Parallel structure or parallelism is the repetition of a particular grammatical form within a sentence, or in this case, between points and sub-points. This simply means that if the first point is a verb , the sub-point should also be a verb.

Example of parallelism:

• Include different regions, focusing on the different arguments from those against immunization

Coordination: Be aware of each point’s weight

Your chosen subheadings should hold the same significance as each other, as should all first sub-points, secondary sub-points, and so on.

Example of coordination:

• Include immunization figures in affected regions
• Illnesses that can result from the measles virus

Subordination: Work from general to specific

Subordination refers to the separation of general points from specific. Your main headings should be quite general, and each level of sub-point should become more specific.

Example of subordination:

Division: break information into sub-points.

Your headings should be divided into two or more subsections. There is no limit to how many subsections you can include under each heading, but keep in mind that the information will be structured into a paragraph during the writing stage, so you should not go overboard with the number of sub-points.

Ready to start writing or looking for guidance on a different step in the process? Read our step-by-step guide on how to write a research paper .

Cite this Scribbr article

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Gahan, C. (2023, August 15). How to Create a Structured Research Paper Outline | Example. Scribbr. Retrieved August 12, 2024, from https://www.scribbr.com/research-paper/outline/

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Sociology Research Topics Making Projects Professional

Table of Contents

Are you enthusiastic about investigating sociological issues and phenomena but every time you are assigned to such an assignment you cannot decide on appropriate sociology topics to research? The list of diverse sociology research topics can address the problem of a constant lack of creative ideas. Boost your inspiration!

Sociological Research Strategies

The choice of sociology research topics proves puzzling since it has a few intricacies you should prioritize. Primarily, it is your passion for a selected subject. Another aspect is your competence. It implies that the chosen sociology research paper topics should meet your academic goals. 

The last nuance, which is critical, but learners routinely omit, is a sociological strategy required for investigating selected sociology research topics. Competent top-rated coursework writing service authors distinguish a few major sociological methods that can be employed for revealing sociology research topics thoroughly.

• Information analysis. 

While most inexperienced authors use this method as only one in their studies, it is one of the numerous strategies you can take to deep dive into sociology research paper topics.

• Case studies.

Investigating particular cases independently or gleaning ideas from earlier prepared studies is always a viable option.

Concluding on an in-depth representative sample is more sensible than gathering merely theoretical data without practical evidence.

• Interviews.

Savvy specialists are familiar with insights into research topics for sociology and share their expertise in such conversations.

When you have structured details about sociological research methods and are aware of how to employ them in practice, your chances of conveying the main message and revealing sociology topics decently are doubled. Authors familiar with basic methodological techniques no longer face a lack of creative ideas and data while completing research projects. 

Interesting Sociology Research Topics

Sociology topics to research comprise questions related to the interaction between diverse cultures, social institutions and their place in people’s socialization, personal identity determination, educational systems and their effect on the economic development of the states, political systems, place of technology in social classes stratification, behavior patterns, and many other sociology research topics you can investigate in your sociology research paper . 

Sociology research topics are routinely categorized by aspects they cover within sociology as a science. However, ten interesting sociology research topics on various sociological niches are gathered below to highlight the most pressing issues in the field. Look through the sociology research topics list and glean ideas for custom titles.

• Problems of social mobility and migration processes in the world.
• Sociological interpretation of social self-organization.
• Social stratification measurement criteria.
• Social status: Psychological pressure and biases.
• National mentality influences a country’s brand.
• Traditions and laws are regulators of human behavior.
• Interaction of social and personal values.
• Specificity of social conflicts between individuals of different social statuses.
• Social innovation: Breakthrough in self-acceptance and personal awareness.
• Social process management: Major stakeholders.

Easy sociology research topics

If it is your primary experience of investigating sociology research topics, what about clear-cut but interesting sociology research topics? The straightforward titles do not imply worse results but guarantee sufficient theoretical and empirical information on such subjects. Check a few simple sociology research topics below.

• The social knowledge structure. 
• The role of sociology in modern society formation.
• Empirical sociology in the structure of social knowledge.
• Staging of the formation and development of sociology as an independent science.
• Positive and negative aspects of marginalization.
• Social structure transformation trends.
• Socialization’s role in the person’s awareness of their identity.
• Decision management specifics in interpersonal relationships.
• Models of economic behavior in countries with a high development level.
• Effective methods for fostering employee motivation.

Sociology research topics for high school students

Finding good sociology research topics is not as challenging as opting for ideas that meet the project instructions. If your task description gives you freedom of choice, you can look through the following sociology research topics list and opt for a perfect match to your interests. 

• Peer pressure influences decision-making.
• Teens’ sociological data processing methodology. 
• Factors of young people’s opinion formation. 
• Teen’s social adaptation after emigration. 
• Symbols of national culture.
• Social space interpretation’s effect on youth’s worldviews.
• Social efficiency of law in less developed countries.
• Manifestations of social maturity in practice.
• Ageism is an obstacle to adaptation in the workplace.
• Social capital in economic relations.

Sociology research topics on mental health

Anxiety, mental disorders, lack of sleep, and energy are critical social issues of the current technological generation. If you opt for such sociology research topics and dive deeper into their investigation, chances of completing a project at a decent score are high. Become familiar with good topics for sociology research paper relevant to a huge audience.

• Impact of social inequalities on mental health. 
• Background anxiety is a consequence of excess dopamine. 
• Practices of spreading awareness of the healthy sleep cruciality.
• Can health have a valuable dimension in modern society?
• Mainstreaming the mental disorders issue among young people at the state level.
• Impact of mental health care institutes’ policies on citizens’ decision-making patterns. 
• Dependence of the population’s psychological development level on the state’s social development.
• Stress resistance is the basis of professional health.
• Mental health is an economic and social good.
• The practice of including mental health services in work insurance plans.

Sociology research topics on family

The gap between young people’s and adults’ perceptions of family is growing so the research focus shifts to innovative sociology marriage and family research topics. As sociology is a multifaceted field, the variety of family sociology topics is immense.

• Family roles: Individual’s self-determination. 
• Intergenerational ties and gaps in worldview. 
• Key causes of marriage breakdown: Psychological incompatibilities of partners. 
• Proven practices of building mutual understanding on issues of raising children as a couple. 
• Globalizing systems of marital relations. 
• Psychological trauma at an early age affects the future success of an individual.
• The family’s function of emotional satisfaction.
• Living conditions and psycho-emotional state and their relationship with the mental health of family members.
• Legal protection of low-income families creates development opportunities.
• Leadership in the family: Cultural features.

Sociology research topics on gender

Gender identity, equality, rights, inclusion, and related sociology research topics are currently on the agenda. What research topics for sociology on gender do seem more eye-catching?

• Feminine and masculine traits: Foundations of gender profiling.
• Gender socialization in the family.
• The impact of feminism on worldview. 
• The place of parental instincts in gender determination. 
• Gender social norms: Information pressure.
• Reducing the social distance between people of different genders.
• Women in geopolitics in retrospect.
• Gender identification and stereotypes.
• Staging of the gender formation: Biological and sociological categories.
• Gender polarization issue.

Medical sociology research topics

Healthcare involves many stakeholders and takes a critical place in social life. Interactions between individuals, social phenomena in the medical field, and other sociology topics for research paper are appropriate for in-depth analysis. The issues of society’s perception of chronic and infectious diseases are pressing and may be effective options for sociology topics to research. It is up to you what aspect of healthcare sociology research topics to focus on.

• Pandemics have social phenomena peculiarities.
• Medical assistance and medical care differences in practice.
• Methods of sociological analysis of health care problems.
• The medical care availability’s influence on the mood of the population.
• Social causes and consequences of diseases.
• Spreading the ideas of preventive medicine in society.
• Accelerated trend of infections and viruses spread due to globalization.
• The impact of the aging of the nation on losses in the country’s health care sector.
• Ethics in medical practice.
• The specifics of medical statistics analytics.

Political sociology research paper topics

The scope of political science covers great sociology research topics worthy of discussion in your research project. A vast bulk of surveys, interviews, polls, and statistics are available on the internet and may be potential information material for in-depth study of topics for sociology research paper. Figure out intriguing sociology research topics about politics from the below examples.

• Forecasting the political situation in conditions of instability. 
• Political manipulation: Techniques of taking an advantageous position in the international arena. 
• Social pressure on political elites in retrospect. 
• Political sociology through the prism of philosophy. 
• Distribution of power in society. 
• The political consciousness phenomenon.
• A striking difference between democratic and totalitarian political institutions. 
• Political opposition: Strong advantages and pitfalls.
• Political privileges boost social stratification.
• Political parties’ evolution determines a country’s involved potential.

The diversity of sociology research topics rarely leads to a lack of creativity in opting for appropriate sociology topics for investigation. The above list of sociology research paper topics proves the facts. Nevertheless, learners face the wide selection of sociology research topics as a puzzling challenge having no idea how to finally decide what are genuinely good sociology research topics. Look through expert research topics in sociology and craft authentic social studies topics focusing on your needs and practical experience. Properly formulated sociology research topics are half of success!

100+ Social Studies Topics to Inspire Your Next Essay

Anorexia essay: how to work with a scary topic and do it right, how to write movie titles in essays.

9 Best Marketing Research Methods to Know Your Buyer Better [+ Examples]

Published: August 08, 2024

One of the most underrated skills you can have as a marketer is marketing research — which is great news for this unapologetic cyber sleuth.

From brand design and product development to buyer personas and competitive analysis, I’ve researched a number of initiatives in my decade-long marketing career.

And let me tell you: having the right marketing research methods in your toolbox is a must.

Market research is the secret to crafting a strategy that will truly help you accomplish your goals. The good news is there is no shortage of options.

How to Choose a Marketing Research Method

Thanks to the Internet, we have more marketing research (or market research) methods at our fingertips than ever, but they’re not all created equal. Let’s quickly go over how to choose the right one.

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1. Identify your objective.

What are you researching? Do you need to understand your audience better? How about your competition? Or maybe you want to know more about your customer’s feelings about a specific product.

Before starting your research, take some time to identify precisely what you’re looking for. This could be a goal you want to reach, a problem you need to solve, or a question you need to answer.

For example, an objective may be as foundational as understanding your ideal customer better to create new buyer personas for your marketing agency (pause for flashbacks to my former life).

Or if you’re an organic sode company, it could be trying to learn what flavors people are craving.

2. Determine what type of data and research you need.

Next, determine what data type will best answer the problems or questions you identified. There are primarily two types: qualitative and quantitative. (Sound familiar, right?)

• Qualitative Data is non-numerical information, like subjective characteristics, opinions, and feelings. It’s pretty open to interpretation and descriptive, but it’s also harder to measure. This type of data can be collected through interviews, observations, and open-ended questions.
• Quantitative Data , on the other hand, is numerical information, such as quantities, sizes, amounts, or percentages. It’s measurable and usually pretty hard to argue with, coming from a reputable source. It can be derived through surveys, experiments, or statistical analysis.

Understanding the differences between qualitative and quantitative data will help you pinpoint which research methods will yield the desired results.

For instance, thinking of our earlier examples, qualitative data would usually be best suited for buyer personas, while quantitative data is more useful for the soda flavors.

However, truth be told, the two really work together.

Qualitative conclusions are usually drawn from quantitative, numerical data. So, you’ll likely need both to get the complete picture of your subject.

For example, if your quantitative data says 70% of people are Team Black and only 30% are Team Green — Shout out to my fellow House of the Dragon fans — your qualitative data will say people support Black more than Green.

(As they should.)

Primary Research vs Secondary Research

You’ll also want to understand the difference between primary and secondary research.

Primary research involves collecting new, original data directly from the source (say, your target market). In other words, it’s information gathered first-hand that wasn’t found elsewhere.

Some examples include conducting experiments, surveys, interviews, observations, or focus groups.

Meanwhile, secondary research is the analysis and interpretation of existing data collected from others. Think of this like what we used to do for school projects: We would read a book, scour the internet, or pull insights from others to work from.

So, which is better?

Personally, I say any research is good research, but if you have the time and resources, primary research is hard to top. With it, you don’t have to worry about your source's credibility or how relevant it is to your specific objective.

You are in full control and best equipped to get the reliable information you need.

3. Put it all together.

Once you know your objective and what kind of data you want, you’re ready to select your marketing research method.

For instance, let’s say you’re a restaurant trying to see how attendees felt about the Speed Dating event you hosted last week.

You shouldn’t run a field experiment or download a third-party report on speed dating events; those would be useless to you. You need to conduct a survey that allows you to ask pointed questions about the event.

This would yield both qualitative and quantitative data you can use to improve and bring together more love birds next time around.

Best Market Research Methods for 2024

Now that you know what you’re looking for in a marketing research method, let’s dive into the best options.

Note: According to HubSpot’s 2024 State of Marketing report, understanding customers and their needs is one of the biggest challenges facing marketers today. The options we discuss are great consumer research methodologies , but they can also be used for other areas.

Primary Research

1. interviews.

Interviews are a form of primary research where you ask people specific questions about a topic or theme. They typically deliver qualitative information.

I’ve conducted many interviews for marketing purposes, but I’ve also done many for journalistic purposes, like this profile on comedian Zarna Garg . There’s no better way to gather candid, open-ended insights in my book, but that doesn’t mean they’re a cure-all.

What I like: Real-time conversations allow you to ask different questions if you’re not getting the information you need. They also push interviewees to respond quickly, which can result in more authentic answers.

What I dislike: They can be time-consuming and harder to measure (read: get quantitative data) unless you ask pointed yes or no questions.

Best for: Creating buyer personas or getting feedback on customer experience, a product, or content.

2. Focus Groups

Focus groups are similar to conducting interviews but on a larger scale.

In marketing and business, this typically means getting a small group together in a room (or Zoom), asking them questions about various topics you are researching. You record and/or observe their responses to then take action.

They are ideal for collecting long-form, open-ended feedback, and subjective opinions.

One well-known focus group you may remember was run by Domino’s Pizza in 2009 .

After poor ratings and dropping over $100 million in revenue, the brand conducted focus groups with real customers to learn where they could have done better.

It was met with comments like “worst excuse for pizza I’ve ever had” and “the crust tastes like cardboard.” But rather than running from the tough love, it took the hit and completely overhauled its recipes.

The team admitted their missteps and returned to the market with better food and a campaign detailing their “Pizza Turn Around.”

The result? The brand won a ton of praise for its willingness to take feedback, efforts to do right by its consumers, and clever campaign. But, most importantly, revenue for Domino’s rose by 14.3% over the previous year.

The brand continues to conduct focus groups and share real footage from them in its promotion:

What I like: Similar to interviewing, you can dig deeper and pivot as needed due to the real-time nature. They’re personal and detailed.

What I dislike: Once again, they can be time-consuming and make it difficult to get quantitative data. There is also a chance some participants may overshadow others.

Best for: Product research or development

Pro tip: Need help planning your focus group? Our free Market Research Kit includes a handy template to start organizing your thoughts in addition to a SWOT Analysis Template, Survey Template, Focus Group Template, Presentation Template, Five Forces Industry Analysis Template, and an instructional guide for all of them. Download yours here now.

3. Surveys or Polls

Surveys are a form of primary research where individuals are asked a collection of questions. It can take many different forms.

They could be in person, over the phone or video call, by email, via an online form, or even on social media. Questions can be also open-ended or closed to deliver qualitative or quantitative information.

A great example of a close-ended survey is HubSpot’s annual State of Marketing .

In the State of Marketing, HubSpot asks marketing professionals from around the world a series of multiple-choice questions to gather data on the state of the marketing industry and to identify trends.

The survey covers various topics related to marketing strategies, tactics, tools, and challenges that marketers face. It aims to provide benchmarks to help you make informed decisions about your marketing.

It also helps us understand where our customers’ heads are so we can better evolve our products to meet their needs.

Apple is no stranger to surveys, either.

In 2011, the tech giant launched Apple Customer Pulse , which it described as “an online community of Apple product users who provide input on a variety of subjects and issues concerning Apple.”

"For example, we did a large voluntary survey of email subscribers and top readers a few years back."

While these readers gave us a long list of topics, formats, or content types they wanted to see, they sometimes engaged more with content types they didn’t select or favor as much on the surveys when we ran follow-up ‘in the wild’ tests, like A/B testing.”  

Pepsi saw similar results when it ran its iconic field experiment, “The Pepsi Challenge” for the first time in 1975.

The beverage brand set up tables at malls, beaches, and other public locations and ran a blindfolded taste test. Shoppers were given two cups of soda, one containing Pepsi, the other Coca-Cola (Pepsi’s biggest competitor). They were then asked to taste both and report which they preferred.

People overwhelmingly preferred Pepsi, and the brand has repeated the experiment multiple times over the years to the same results.

What I like: It yields qualitative and quantitative data and can make for engaging marketing content, especially in the digital age.

What I dislike: It can be very time-consuming. And, if you’re not careful, there is a high risk for scientific error.

Best for: Product testing and competitive analysis

Pro tip:  " Don’t make critical business decisions off of just one data set," advises Pamela Bump. "Use the survey, competitive intelligence, external data, or even a focus group to give you one layer of ideas or a short-list for improvements or solutions to test. Then gather your own fresh data to test in an experiment or trial and better refine your data-backed strategy."

Secondary Research

8. public domain or third-party research.

While original data is always a plus, there are plenty of external resources you can access online and even at a library when you’re limited on time or resources.

Some reputable resources you can use include:

• Pew Research Center
• McKinley Global Institute
• Relevant Global or Government Organizations (i.e United Nations or NASA)

It’s also smart to turn to reputable organizations that are specific to your industry or field. For instance, if you’re a gardening or landscaping company, you may want to pull statistics from the Environmental Protection Agency (EPA).

If you’re a digital marketing agency, you could look to Google Research or HubSpot Research . (Hey, I know them!)

What I like: You can save time on gathering data and spend more time on analyzing. You can also rest assured the data is from a source you trust.

What I dislike: You may not find data specific to your needs.

Best for: Companies under a time or resource crunch, adding factual support to content

Pro tip: Fellow HubSpotter Iskiev suggests using third-party data to inspire your original research. “Sometimes, I use public third-party data for ideas and inspiration. Once I have written my survey and gotten all my ideas out, I read similar reports from other sources and usually end up with useful additions for my own research.”

9. Buy Research

If the data you need isn’t available publicly and you can’t do your own market research, you can also buy some. There are many reputable analytics companies that offer subscriptions to access their data. Statista is one of my favorites, but there’s also Euromonitor , Mintel , and BCC Research .

What I like: Same as public domain research

What I dislike: You may not find data specific to your needs. It also adds to your expenses.

Best for: Companies under a time or resource crunch or adding factual support to content

Which marketing research method should you use?

You’re not going to like my answer, but “it depends.” The best marketing research method for you will depend on your objective and data needs, but also your budget and timeline.

My advice? Aim for a mix of quantitative and qualitative data. If you can do your own original research, awesome. But if not, don’t beat yourself up. Lean into free or low-cost tools . You could do primary research for qualitative data, then tap public sources for quantitative data. Or perhaps the reverse is best for you.

Whatever your marketing research method mix, take the time to think it through and ensure you’re left with information that will truly help you achieve your goals.

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