Guidance for umbrella reviews of observational studies: A scoping review
Bibliographic record
Abstract
Background: Umbrella reviews, or overviews of reviews, synthesize information using systematic reviews (SRs) as their unit of analysis. Although a formal guideline exists for reporting umbrella reviews of healthcare interventions (i.e. Preferred Reporting Items for Overviews of Reviews [PRIOR]), no formal guideline exists for conducting and/or reporting umbrella reviews of observational studies that examine epidemiological associations. Objective: To review the existing guidance on conducting and/or reporting umbrella reviews of observational studies on epidemiological associations, as part of the process of developing a formal reporting guideline. Methods: We reviewed the scoping review conducted in the context of PRIOR development and identified documents through forward citation search in PubMed, Scopus, and manual search in Google Scholar, Google Search up to December 22, 2024. Documents, regardless of format, were included if they provided guidance for conducting and/or reporting umbrella reviews of observational studies (including meta-research studies of their features). Title/abstract screening and data extraction were performed independently and in duplicate and summarized narratively by stages of the umbrella review process. Results: The search retrieved 4491 unique records, with 96 full texts assessed and eight documents included. These documents, published between 2014 and 2023, offered guidance across seven topic areas, but overall guidance on conducting and/or reporting is limited. These areas include the answerable questions, prerequisite considerations, the scope of umbrella reviews, searching for SRs, primary data collection, analysis, presentation, and assessing the certainty/quality of the body of evidence. Conclusion: There is a need for dedicated, practical, and evidence-based formal reporting guidelines for umbrella reviews of observational studies on epidemiological associations. This review lays the groundwork for developing the PRIOR-extension for such studies: the Preferred Reporting Items for Umbrella Reviews of Cross-sectional, Case-control, and Cohort Studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.134 | 0.285 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.037 | 0.011 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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; both teacher heads 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".