Rapid reviews methods series: assessing the appropriateness of conducting a rapid review
Bibliographic record
Abstract
This paper, part of the Cochrane Rapid Review Methods Group series, offers guidance on determining when to conduct a rapid review (RR) instead of a full systematic review (SR). While both review types aim to comprehensively synthesise evidence, RRs, conducted within a shorter time frame of typically 6 months or less, involve streamlined methods to expedite the process. The decision to opt for an RR depends on the urgency of the research question, resource availability and the impact on decision outcomes. The paper categorises scenarios where RRs are appropriate, including urgent decision-making, informing guidelines, assessing new technologies and identifying evidence gaps. It also outlines instances when RRs may be inappropriate, cautioning against conducting them solely for ease, quick publication or only cost-saving motives.When deciding on an RR, it is crucial to consider both conceptual and practical factors. These factors encompass the urgency of needing timely evidence, the consequences of waiting for a full SR, the potential risks associated with incomplete evidence, and the risk of not using synthesised evidence in decision-making, among other considerations. Key factors to weigh also include having a clearly defined need, a manageable scope and access to the necessary expertise. Overall, this paper aims to guide informed judgements about whether to choose an RR over an SR based on the specific research question and context. Researchers and decision-makers are encouraged to carefully weigh potential trade-offs when opting for RRs.
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
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 | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
| gpt | Metaresearch Domain: Methods · 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 | 0.697 | 0.913 |
| Meta-epidemiology (narrow) | 0.006 | 0.010 |
| Meta-epidemiology (broad) | 0.011 | 0.015 |
| Bibliometrics | 0.034 | 0.032 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.021 | 0.020 |
| Open science | 0.009 | 0.014 |
| Research integrity | 0.011 | 0.014 |
| Insufficient payload (model declined to judge) | 0.103 | 0.077 |
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".