Guidance for overviews of reviews continues to accumulate, but important challenges remain: a scoping review
Bibliographic record
Abstract
BACKGROUND: Overviews of reviews (overviews) provide an invaluable resource for healthcare decision-making by combining large volumes of systematic review (SR) data into a single synthesis. The production of high-quality overviews hinges on the availability of practical evidence-based guidance for conduct and reporting. OBJECTIVES: Within the broad purpose of informing the development of a reporting guideline for overviews, we aimed to provide an up-to-date map of existing guidance related to the conduct of overviews, and to identify common challenges that authors face when undertaking overviews. METHODS: We updated a scoping review published in 2016 using the search methods that had produced the highest yield: ongoing reference tracking (2014 to March 2020 in PubMed, Scopus, and Google Scholar), hand-searching conference proceedings and websites, and contacting authors of published overviews. Using a qualitative meta-summary approach, one reviewer extracted, organized, and summarized the guidance and challenges presented within the included documents. A second reviewer verified the data and synthesis. RESULTS: We located 28 new guidance documents, for a total of 77 documents produced by 34 research groups. The new guidance helps to resolve some earlier identified challenges in the production of overviews. Important developments include strengthened guidance on handling primary study overlap at the study selection and analysis stages. Despite marked progress, several areas continue to be hampered by inconsistent or lacking guidance. There is ongoing debate about whether, when, and how supplemental primary studies should be included in overviews. Guidance remains scant on how to extract and use appraisals of quality of the primary studies within the included SRs and how to adapt GRADE methodology to overviews. The challenges that overview authors face are often related to the above-described steps in the process where evidence-based guidance is lacking or conflicting. CONCLUSION: The rising popularity of overviews has been accompanied by a steady accumulation of new, and sometimes conflicting, guidance. While recent guidance has helped to address some of the challenges that overview authors face, areas of uncertainty remain. Practical tools supported by empirical evidence are needed to assist authors with the many methodological decision points that are encountered in the production of overviews.
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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 | 0.573 | 0.833 |
| Meta-epidemiology (narrow) | 0.005 | 0.007 |
| Meta-epidemiology (broad) | 0.018 | 0.018 |
| Bibliometrics | 0.047 | 0.047 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.036 | 0.042 |
| Open science | 0.015 | 0.018 |
| Research integrity | 0.027 | 0.025 |
| Insufficient payload (model declined to judge) | 0.017 | 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".