Rapid review method series: interim guidance for the reporting of rapid reviews
Bibliographic record
Abstract
Rapid reviews (RRs) are produced using abbreviated methods compared with standard systematic reviews (SR) to expedite the process for decision-making. This paper provides interim guidance to support the complete reporting of RRs. Recommendations emerged from a survey informed by empirical studies of RR reporting, in addition to collective experience. RR producers should use existing, robustly developed reporting guidelines as the foundation for writing RRs: notably Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 (PRISMA 2020; reporting for SRs), but also preferred reporting items for overviews of reviews (PRIOR) items (reporting for overviews of SRs) where SRs are included in the RR. In addition, a minimum set of six items were identified for RRs: three items pertaining to methods and three addressing publication ethics. Authors should be reporting what a priori-defined iterative methods were used during conduct, what distinguishes their RR from an SR, and knowledge user (eg, policymaker) involvement in the process. Explicitly reporting deviations from standard SR methods, including omitted steps, is important. The inclusion of publication ethics items reflects the predominance of non-journal published RRs: reporting an authorship byline and corresponding author, acknowledging other contributors, and reporting the use of expert peer review. As various formats may be used when packaging and presenting information to decision-makers, it is practical to think of complete reporting as across a set of explicitly linked documents made available in an open-access journal or repository that is barrier-free. We encourage feedback from the RR community of the use of these items as we look to develop a consolidated list in the development of PRISMA-RR.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Reporting · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
| gpt | MetaresearchResearch integrity Domain: Reporting · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
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 | 0.495 | 0.735 |
| Meta-epidemiology (narrow) | 0.008 | 0.017 |
| Meta-epidemiology (broad) | 0.013 | 0.019 |
| Bibliometrics | 0.027 | 0.030 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.020 | 0.016 |
| Open science | 0.010 | 0.010 |
| Research integrity | 0.016 | 0.028 |
| Insufficient payload (model declined to judge) | 0.171 | 0.206 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".