About this platform

Overview for Reporting Guideline developers

This platform was made by James Harwood at the Oxford branch of the UK EQUATOR Centre. Its design and architecture were informed by a programme of research exploring why researchers often fail to adhere to reporting guidelines. This programme included a qualitative evidence synthesis, review of surveys, workshops and focus groups with experts, and interviews with researchers. It can be read here

As a reporting guideline developer, you have control over your own guideline content. To get you started, we set up a blank repository for you in GitHub.

  1. You add (or edit) your guideline content. Reporting guidelines are stored as simple text files and metadata. There is one text file per reporting item. You can add images, embed videos, pop-up definitions, links to training or other help, and you can annotate examples of good and bad reporting. In the future we hope to offer a simple interface for editing content, but for now you can edit files directly within GitHub from your browser or from your computer if you are comfortable using git.

  2. You notify us that your guideline is ready for publication using GitHub’s Release button. Tests run automatically to make sure your files are in the right structure and have the necessary metadata. If these tests pass, we create a version of your guideline using Zenodo. This means researchers can cite the exact version of your guideline that they followed.

  3. Resources are generated from your files. These resources currently include:

    • Web pages: Your reporting guideline will have an overview page that tells users how to use your guideline, what research it applies to, links to other guidelines (that you specify), and a summary of the guidance items. Each reporting item has its own page. All pages have metadata that optimizes them for search engines, large language models, and chat bots.
    • Checklists: Downloadable Word files, where each item links back to the item’s web page.
    • Discussion Boards: Each item links to a discussion board where researchers can ask questions and give feedback.
    • Future resources: In the future, you will be able to generate other resources including AI agents and writing templates.

  4. The website is updated with your new resources.

  5. We run some more automated tests before the updated website is published.

  6. When researchers leave comments on your reporting items’ discussion boards, these comment threads are saved to your GitHub repository so you will be notified of feedback. You can then update your reporting guideline in response to this feedback.

Technical overview

  1. The platform is a static website hosted on GitHub pages. Each reporting guideline is hosted in its own GitHub repository, and included in the main platform’s repository as submodules. This means reporting guideline developers can edit their own content. Reporting guideline developers trigger a release when they want to publish an update.
  2. All repositories use continuous integration, whereby GitHub Actions runs unit, system, and integration tests. These tests must pass for commits to the master branch and releases to be accepted.
  3. If tests pass, GitHub actions notifies Zenodo to update DOI so that the version becomes citable and notifies the main platform repository to pull the updated guideline. The main platform pulls the updated DOI from Zenodo, runs some Python scripts to regenerate reporting guideline resources. By default these include a checklist file, a summary webpage, webpages for each reporting item, and discussion board pages for each item. Python generates these resources as Markdown files.
  4. Quarto converts these Markdown files into other formats (HTML for web pages, Microsoft Word for documents etc).
  5. A second set of automated tests validate the generated resources and website.
  6. Once built, the website is served using GitHub Pages. Each reporting guideline item has its own Giscus discussion board, where comments are stored as GitHub Discussions linked to the relevant file stored in the reporting guideline repository.

Reporting Guidelines are recommendations to help describe your work clearly

Your research will be used by people from different disciplines and backgrounds for decades to come. Reporting guidelines list the information you should describe so that everyone can understand, replicate, and synthesise your work.

Reporting guidelines do not prescribe how research should be designed or conducted. Rather, they help authors transparently describe what they did, why they did it, and what they found.

Reporting guidelines make writing research easier, and transparent research leads to better patient outcomes.

Easier writing

Following guidance makes writing easier and quicker.

Learn more »

Smoother publishing

Many journals require completed reporting checklists at submission.

Learn more »

Maximum impact

From nobel prizes to null results, articles have more impact when everyone can use them.

Learn more »

Who reads research?

You work will be read by different people, for different reasons, around the world, and for decades to come. Reporting guidelines help you consider all of your potential audiences. For example, your research may be read by researchers from different fields, by clinicians, patients, evidence synthesisers, peer reviewers, or editors. Your readers will need information to understand, to replicate, apply, appraise, synthesise, and use your work.

Cohort studies

A cohort study is an observational study in which a group of people with a particular exposure (e.g. a putative risk factor or protective factor) and a group of people without this exposure are followed over time. The outcomes of the people in the exposed group are compared to the outcomes of the people in the unexposed group to see if the exposure is associated with particular outcomes (e.g. getting cancer or length of life).

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Case-control studies

A case-control study is a research method used in healthcare to investigate potential risk factors for a specific disease. It involves comparing individuals who have been diagnosed with the disease (cases) to those who have not (controls). By analysing the differences between the two groups, researchers can identify factors that may contribute to the development of the disease.

An example would be when researchers conducted a case-control study examining whether exposure to diesel exhaust particles increases the risk of respiratory disease in underground miners. Cases included miners diagnosed with respiratory disease, while controls were miners without respiratory disease. Participants' past occupational exposures to diesel exhaust particles were evaluated to compare exposure rates between cases and controls.

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Cross-sectional studies

A cross-sectional study (also sometimes called a "cross-sectional survey") serves as an observational tool, where researchers capture data from a cohort of participants at a singular point. This approach provides a 'snapshot'— a brief glimpse into the characteristics or outcomes prevalent within a designated population at that precise point in time. The primary aim here is not to track changes or developments over an extended period but to assess and quantify the current situation regarding specific variables or conditions. Such a methodology is instrumental in identifying patterns or correlations among various factors within the population, providing a basis for further, more detailed investigation.

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Systematic reviews

A systematic review is a comprehensive approach designed to identify, evaluate, and synthesise all available evidence relevant to a specific research question. In essence, it collects all possible studies related to a given topic and design, and reviews and analyses their results.

The process involves a highly sensitive search strategy to ensure that as much pertinent information as possible is gathered. Once collected, this evidence is often critically appraised to assess its quality and relevance, ensuring that conclusions drawn are based on robust data. Systematic reviews often involve defining inclusion and exclusion criteria, which help to focus the analysis on the most relevant studies, ultimately synthesising the findings into a coherent narrative or statistical synthesis. Some systematic reviews will include a [meta-analysis]{.defined data-bs-toggle="offcanvas" href="#glossaryItemmeta_analyses" aria-controls="offcanvasExample" role="button"}.

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Systematic review protocols

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Meta analyses of Observational Studies

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Randomised Trials

A randomised controlled trial (RCT) is a trial in which participants are randomly assigned to one of two or more groups: the experimental group or groups receive the intervention or interventions being tested; the comparison group (control group) receive usual care or no treatment or a placebo. The groups are then followed up to see if there are any differences between the results. This helps in assessing the effectiveness of the intervention.

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Randomised Trial Protocols

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Qualitative research

Research that aims to gather and analyse non-numerical (descriptive) data in order to gain an understanding of individuals' social reality, including understanding their attitudes, beliefs, and motivation. This type of research typically involves in-depth interviews, focus groups, or field observations in order to collect data that is rich in detail and context. Qualitative research is often used to explore complex phenomena or to gain insight into people's experiences and perspectives on a particular topic. It is particularly useful when researchers want to understand the meaning that people attach to their experiences or when they want to uncover the underlying reasons for people's behaviour. Qualitative methods include ethnography, grounded theory, discourse analysis, and interpretative phenomenological analysis.

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Case Reports

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Diagnostic Test Accuracy Studies

Diagnostic accuracy studies focus on estimating the ability of the test(s) to correctly identify people with a predefined target condition, or the condition of interest (sensitivity) as well as to clearly identify those without the condition (specificity).

Prediction Models

Prediction model research is used to test the accurarcy of a model or test in estimating an outcome value or risk. Most models estimate the probability of the presence of a particular health condition (diagnostic) or whether a particular outcome will occur in the future (prognostic). Prediction models are used to support clinical decision making, such as whether to refer patients for further testing, monitor disease deterioration or treatment effects, or initiate treatment or lifestyle changes. Examples of well known prediction models include EuroSCORE II for cardiac surgery, the Gail model for breast cancer, the Framingham risk score for cardiovascular disease, IMPACT for traumatic brain injury, and FRAX for osteoporotic and hip fractures.

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Animal Research

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Quality Improvement in Healthcare

Quality improvement research is about finding out how to improve and make changes in the most effective way. It is about systematically and rigourously exploring "what works" to improve quality in healthcare and the best ways to measure and disseminate this to ensure positive change. Most quality improvement effectiveness research is conducted in hospital settings, is focused on multiple quality improvement interventions, and uses process measures as outcomes. There is a great deal of variation in the research designs used to examine quality improvement effectiveness.

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Economic Evaluations in Healthcare

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Meta Analyses

A meta-analysis is a statistical technique that amalgamates data from multiple studies to yield a single estimate of the effect size. This approach enhances precision and offers a more comprehensive understanding by integrating quantitative findings. Central to a meta-analysis is the evaluation of heterogeneity, which examines variations in study outcomes to ensure that differences in populations, interventions, or methodologies do not skew results. Techniques such as meta-regression or subgroup analysis are frequently employed to explore how various factors might influence the outcomes. This method is particularly effective when aiming to quantify the effect size, odds ratio, or risk ratio, providing a clearer numerical estimate that can significantly inform clinical or policy decisions.

How Meta-analyses and Systematic Reviews Work Together

Systematic reviews and meta-analyses function together, each complementing the other to provide a more robust understanding of research evidence. A systematic review meticulously gathers and evaluates all pertinent studies, establishing a solid foundation of qualitative and quantitative data. Within this framework, if the collected data exhibit sufficient homogeneity, a meta-analysis can be performed. This statistical synthesis allows for the integration of quantitative results from individual studies, producing a unified estimate of effect size. Techniques such as meta-regression or subgroup analysis may further refine these findings, elucidating how different variables impact the overall outcome. By combining these methodologies, researchers can achieve both a comprehensive narrative synthesis and a precise quantitative measure, enhancing the reliability and applicability of their conclusions. This integrated approach ensures that the findings are not only well-rounded but also statistically robust, providing greater confidence in the evidence base.

Why Don't All Systematic Reviews Use a Meta-Analysis?

Systematic reviews do not always have meta-analyses, due to variations in the data. For a meta-analysis to be viable, the data from different studies must be sufficiently similar, or homogeneous, in terms of design, population, and interventions. When the data shows significant heterogeneity, meaning there are considerable differences among the studies, combining them could lead to skewed or misleading conclusions. Furthermore, the quality of the included studies is critical; if the studies are of low methodological quality, merging their results could obscure true effects rather than explain them.

Protocol

A plan or set of steps that defines how something will be done. Before carrying out a research study, for example, the research protocol sets out what question is to be answered and how information will be collected and analysed.

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