DEFINING RAPID REVIEWS: A MODIFIED DELPHI CONSENSUS APPROACH
Bibliographic record
Abstract
OBJECTIVES: Rapid reviews are characterized as an accelerated evidence synthesis approach with no universally accepted methodology or definition. This modified Delphi consensus study aimed to develop a comprehensive set of defining characteristics for rapid reviews that may be used as a functional definition. METHODS: Expert panelists with knowledge in rapid reviews and evidence synthesis were identified. In the first round, panelists were asked to answer a seventeen-item survey addressing a variety of rapid review topics. Results led to the development of statements describing the characteristics of rapid reviews that were circulated to experts for agreement in a second survey round and further revised in a third round. Consensus was reached if ≥70 percent of experts agreed and there was stability in free-text comments. RESULTS: A panel of sixty-six experts participated. Consensus was reached on ten of eleven statements describing the characteristics of rapid reviews. According to the panel, rapid reviews aim to meet the requirements and timelines of a decision maker and should be conducted in less time than a systematic review. They use a variety of approaches to accelerate the evidence synthesis process, tailor the methods conventionally used to carry out systematic reviews, and use the most rigorous methods that the delivery time frame will allow. CONCLUSIONS: This study achieved consensus on ten statements describing the defining characteristics of rapid reviews based on the opinion of a panel of knowledgeable experts. Areas of disagreement were also highlighted. Findings emphasize the role of the decision maker and stress the importance of transparent reporting.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.404 | 0.439 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.014 | 0.009 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.006 | 0.019 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".