Protocol for the development of a reporting guideline for umbrella reviews on epidemiological associations using cross-sectional, case-control and cohort studies: the Preferred Reporting Items for Umbrella Reviews of Cross-sectional, Case-control and Cohort studies (PRIUR-CCC)
Bibliographic record
Abstract
INTRODUCTION: Observational studies are fraught with several biases including reverse causation and residual confounding. Overview of reviews of observational studies (ie, umbrella reviews) synthesise systematic reviews with or without meta-analyses of cross-sectional, case-control and cohort studies, and may also aid in the grading of the credibility of reported associations. The number of published umbrella reviews has been increasing. Recently, a reporting guideline for overviews of reviews of healthcare interventions (Preferred Reporting Items for Overviews of Reviews (PRIOR)) was published, but the field lacks reporting guidelines for umbrella reviews of observational studies. Our aim is to develop a reporting guideline for umbrella reviews on cross-sectional, case-control and cohort studies assessing epidemiological associations. METHODS AND ANALYSIS: We will adhere to established guidance and prepare a PRIOR extension for systematic reviews of cross-sectional, case-control and cohort studies testing epidemiological associations between an exposure and an outcome, namely Preferred Reporting Items for Umbrella Reviews of Cross-sectional, Case-control and Cohort studies (PRIUR-CCC). Step 1 will be the project launch to identify stakeholders. Step 2 will be a literature review of available guidance to conduct umbrella reviews. Step 3 will be an online Delphi study sampling 100 participants among authors and editors of umbrella reviews. Step 4 will encompass the finalisation of PRIUR-CCC statement, including a checklist, a flow diagram, explanation and elaboration document. Deliverables will be (i) identifying stakeholders to involve according to relevant expertise and end-user groups, with an equity, diversity and inclusion lens; (ii) completing a narrative review of methodological guidance on how to conduct umbrella reviews, a narrative review of methodology and reporting in published umbrella reviews and preparing an initial PRIUR-CCC checklist for Delphi study round 1; (iii) preparing a PRIUR-CCC checklist with guidance after Delphi study; (iv) publishing and disseminating PRIUR-CCC statement. ETHICS AND DISSEMINATION: PRIUR-CCC has been approved by The Ottawa Health Science Network Research Ethics Board and has obtained consent (20220639-01H). Participants to step 3 will give informed consent. PRIUR-CCC steps will be published in a peer-reviewed journal and will guide reporting of umbrella reviews on epidemiological associations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.349 | 0.515 |
| Meta-epidemiology (narrow) | 0.006 | 0.010 |
| Meta-epidemiology (broad) | 0.012 | 0.018 |
| Bibliometrics | 0.017 | 0.018 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.017 | 0.013 |
| Open science | 0.008 | 0.011 |
| Research integrity | 0.017 | 0.020 |
| Insufficient payload (model declined to judge) | 0.106 | 0.069 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".