Data analysis and presentation methods in umbrella reviews/overviews of reviews in health care: A cross-sectional study
Bibliographic record
Abstract
Umbrella reviews (URs) synthesize findings from multiple systematic reviews on a specific topic. Methodological approaches for analyzing and presenting UR results vary, and reviewers often adapt methods to align with research objectives. This study examined the characteristics of analysis and presentation methods used in healthcare-related URs. A systematic PubMed search identified URs published between 2023 and 2024. Inclusion criteria focused on healthcare URs using systematic reviews as the unit of analysis. A random sample of 100 eligible URs was included. A customized, piloted data extraction form was used to collect bibliographic, conduct, and reporting data independently. Descriptive analysis and narrative synthesis summarized findings. The most common terminology for eligible studies was "umbrella reviews" (65%) or "overviews" (30%). Question frameworks included PICO (43%) and PICOS (14%), with quantitative systematic reviews included in most URs (98%), and 68% including randomized controlled trials. The most frequent methodological guidance source was Cochrane (32%). Data analysis commonly used narrative synthesis and meta-analysis, with Stata, RevMan, and GRADEPro GDT employed for presentation. Information about study overlap and certainty assessment was rarely reported.Variation exists in how data are analyzed and presented in URs, with key elements often omitted. These findings highlight the need for clearer methodological guidance to enhance consistency and reporting in future URs.
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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 | 0.647 | 0.836 |
| Meta-epidemiology (narrow) | 0.003 | 0.006 |
| Meta-epidemiology (broad) | 0.009 | 0.015 |
| Bibliometrics | 0.046 | 0.064 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.017 | 0.019 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".