What guidance is available for researchers conducting overviews of reviews of healthcare interventions? A scoping review and qualitative metasummary
Bibliographic record
Abstract
BACKGROUND: Overviews of reviews (overviews) compile data from multiple systematic reviews to provide a single synthesis of relevant evidence for decision-making. Despite their increasing popularity, there is limited methodological guidance available for researchers wishing to conduct overviews. The objective of this scoping review is to identify and collate all published and unpublished documents containing guidance for conducting overviews examining the efficacy, effectiveness, and/or safety of healthcare interventions. Our aims were to provide a map of existing guidance documents; identify similarities, differences, and gaps in the guidance contained within these documents; and identify common challenges involved in conducting overviews. METHODS: We conducted an iterative and extensive search to ensure breadth and comprehensiveness of coverage. The search involved reference tracking, database and web searches (MEDLINE, EMBASE, DARE, Scopus, Cochrane Methods Studies Database, Google Scholar), handsearching of websites and conference proceedings, and contacting overview producers. Relevant guidance statements and challenges encountered were extracted, edited, grouped, abstracted, and presented using a qualitative metasummary approach. RESULTS: We identified 52 guidance documents produced by 19 research groups. Relatively consistent guidance was available for the first stages of the overview process (deciding when and why to conduct an overview, specifying the scope, and searching for and including systematic reviews). In contrast, there was limited or conflicting guidance for the latter stages of the overview process (quality assessment of systematic reviews and their primary studies, collecting and analyzing data, and assessing quality of evidence), and many of the challenges identified were also related to these stages. An additional, overarching challenge identified was that overviews are limited by the methods, reporting, and coverage of their included systematic reviews. CONCLUSIONS: This compilation of methodological guidance for conducting overviews of healthcare interventions will facilitate the production of future overviews and can help authors address key challenges they are likely to encounter. The results of this project have been used to identify areas where future methodological research is required to generate empirical evidence for overview methods. Additionally, these results have been used to update the chapter on overviews in the next edition of the Cochrane Handbook for Systematic Reviews of Interventions.
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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 | 0.631 | 0.822 |
| Meta-epidemiology (narrow) | 0.006 | 0.010 |
| Meta-epidemiology (broad) | 0.020 | 0.022 |
| Bibliometrics | 0.077 | 0.056 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.028 | 0.043 |
| Open science | 0.012 | 0.019 |
| Research integrity | 0.016 | 0.011 |
| Insufficient payload (model declined to judge) | 0.017 | 0.006 |
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".