What are the most important unanswered research questions on rapid review methodology? A James Lind Alliance research methodology Priority Setting Partnership: the Priority III study protocol
Bibliographic record
Abstract
<ns3:p> <ns3:bold>Background:</ns3:bold> The value of rapid reviews in informing health care decisions is more evident since the onset of the coronavirus disease 2019 (COVID-19) pandemic. While systematic reviews can be completed rapidly, rapid reviews are usually a type of evidence synthesis in which components of the systematic review process may be simplified or omitted to produce information more efficiently within constraints of time, expertise, funding or any combination thereof. There is an absence of high-quality evidence underpinning some decisions about how we plan, do and share rapid reviews. We will conduct a modified James Lind Alliance Priority Setting Partnership to determine the top 10 unanswered research questions about how we plan, do and share rapid reviews in collaboration with patients, public, reviewers, researchers, clinicians, policymakers and funders. </ns3:p> <ns3:p> <ns3:bold>Methods:</ns3:bold> An international steering group consisting of key stakeholder perspectives (patients, the public, reviewers, researchers, clinicians, policymakers and funders) will facilitate broad reach, recruitment and participation across stakeholder groups. An initial online survey will identify stakeholders’ perceptions of research uncertainties about how we plan, do and share rapid reviews. Responses will be categorised to generate a long list of questions. The list will be checked against systematic reviews published within the past three years to identify if the question is unanswered. A second online stakeholder survey will rank the long list in order of priority. Finally, a virtual consensus workshop of key stakeholders will agree on the top 10 unanswered questions. </ns3:p> <ns3:p> <ns3:bold>Discussion:</ns3:bold> Research prioritisation is an important means for minimising research waste and ensuring that research resources are targeted towards answering the most important questions. Identifying the top 10 rapid review methodology research priorities will help target research to improve how we plan, do and share rapid reviews and ultimately enhance the use of high-quality synthesised evidence to inform health care policy and practice. </ns3:p>
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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 | 0.946 | 0.724 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.023 | 0.005 |
| Bibliometrics | 0.002 | 0.032 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.021 | 0.001 |
| Open science | 0.038 | 0.016 |
| Research integrity | 0.001 | 0.020 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".