Priority III: top 10 rapid review methodology research priorities identified using a James Lind Alliance Priority Setting Partnership
Bibliographic record
Abstract
OBJECTIVES: A rapid review is a form of evidence synthesis considered a resource-efficient alternative to the conventional systematic review. Despite a dramatic rise in the number of rapid reviews commissioned and conducted in response to the coronavirus disease 2019 pandemic, published evidence on the optimal methods of planning, doing, and sharing the results of these reviews is lacking. The Priority III study aimed to identify the top 10 unanswered questions on rapid review methodology to be addressed by future research. STUDY DESIGN AND SETTING: A modified James Lind Alliance Priority Setting Partnership approach was adopted. This approach used two online surveys and a virtual prioritization workshop with patients and the public, reviewers, researchers, clinicians, policymakers, and funders to identify and prioritize unanswered questions. RESULTS: Patients and the public, researchers, reviewers, clinicians, policymakers, and funders identified and prioritized the top 10 unanswered research questions about rapid review methodology. Priorities were identified throughout the entire review process, from stakeholder involvement and formulating the question, to the methods of a systematic review that are appropriate to use, through to the dissemination of results. CONCLUSION: The results of the Priority III study will inform the future research agenda on rapid review methodology. We hope this will enhance the quality of evidence produced by rapid reviews, which will ultimately inform decision-making in the context of healthcare.
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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 | 0.570 | 0.707 |
| Meta-epidemiology (narrow) | 0.004 | 0.007 |
| Meta-epidemiology (broad) | 0.011 | 0.012 |
| Bibliometrics | 0.033 | 0.022 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.038 | 0.015 |
| Open science | 0.009 | 0.027 |
| Research integrity | 0.017 | 0.017 |
| Insufficient payload (model declined to judge) | 0.031 | 0.013 |
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".