An international modified Delphi process supported updating the web-based "right review" tool
Bibliographic record
Abstract
OBJECTIVES: The proliferation of evidence synthesis methods makes it challenging for reviewers to select the ''right'' method. This study aimed to update the Right Review tool (a web-based decision support tool that guides users through a series of questions for recommending evidence synthesis methods) and establish a common set of questions for the synthesis of both quantitative and qualitative studies (https://rightreview.knowledgetranslation.net/). STUDY DESIGN AND SETTING: A 2-round modified international electronic modified Delphi was conducted (2022) with researchers, health-care providers, patients, and policy makers. Panel members rated the importance/clarity of the Right Review tool's guiding questions, evidence synthesis type definitions and tool output. High agreement was defined as at least 70% agreement. Any items not reaching high agreement after round 2 were discussed by the international Project Steering Group. RESULTS: Twenty-four experts from 9 countries completed round 1, with 12 completing round 2. Of the 46 items presented in round 1, 21 reached high agreement. Twenty-seven items were presented in round 2, with 8 reaching high agreement. The Project Steering Group discussed items not reaching high agreement, including 8 guiding questions, 9 review definitions (predominantly related to qualitative synthesis), and 2 output items. Three items were removed entirely and the remaining 16 revised and edited and/or combined with existing items. The final tool comprises 42 items; 9 guiding questions, 25 evidence synthesis definitions and approaches, and 8 tool outputs. CONCLUSION: The freely accessible Right Review tool supports choosing an appropriate review method. The design and clarity of this tool was enhanced by harnessing the Delphi technique to shape ongoing development. The updated tool is expected to be available in Quarter 1, 2025.
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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 | 0.443 | 0.543 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.017 | 0.010 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.022 | 0.008 |
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".