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
Abstract Introduction Observational studies are fraught with several biases including reverse causation and residual confounding, which may limit the credibility of reported associations. Overview of reviews of observational studies (i.e., umbrella reviews) synthesize 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 at a rapid pace. Recently, a reporting guideline for overviews of reviews of healthcare interventions (PRIOR, Preferred Reporting Items for Overviews of Reviews) was published, but the field lacks reporting guidelines for umbrella reviews of observational studies. Thus, our aim is to develop a reporting guideline for umbrella reviews on cross-sectional, case-control, and cohort studies assessing epidemiological associations. Methods and Analyses We will adhere to established guidance on how to develop reporting guidelines in health research and follow four steps to 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 a Delphi study sampling authors and editors of umbrella reviews, Delphi surveys and checklists of epidemiological studies, as well as funders, practitioners, and policy makers, which will be conducted in three rounds. Step 4 will encompass the finalization of PRIUR-CCC statement, including a checklist, a flow diagram, explanation, and elaboration document. Deliverables of each step will be as follows. First, identifying stakeholders to involve according to relevant expertise and end-user groups, with an equity, diversity, and inclusion lens. Second, 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. Third, preparing a PRIUR-CCC checklist with guidance after Delphi study. Fourth, publishing and disseminating PRIUR-CCC statement. Ethics and Dissemination PRIUR-CCC will guide reporting of umbrella reviews on epidemiological associations, with the aim to improve quantitative, credible, and transparent reporting, in a field of evidence synthesis where there is important methodological heterogeneity of reviews, and where sources of bias in original observational studies can lead to misleading conclusions. Strengths This is the first protocol for reporting guidance of umbrella reviews of epidemiological associations This protocol follows the guidance for reporting checklist, which are standard in the field. This protocol is urgently needed given the large number of umbrella reviews on epidemiological associations emerging across different branches of science
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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 | 0.549 | 0.725 |
| Meta-epidemiology (narrow) | 0.006 | 0.009 |
| Meta-epidemiology (broad) | 0.019 | 0.027 |
| Bibliometrics | 0.027 | 0.029 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.021 | 0.012 |
| Open science | 0.014 | 0.013 |
| Research integrity | 0.014 | 0.016 |
| Insufficient payload (model declined to judge) | 0.018 | 0.017 |
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".