Paper 2: Performing rapid reviews
Bibliographic record
Abstract
BACKGROUND: Health policy-makers must often make decisions in compressed time frames and with limited resources. Hence, rapid reviews have become a pragmatic alternative to comprehensive systematic reviews. However, it is important that rapid review methods remain rigorous to support good policy development and decisions. There is currently little evidence about which streamlined steps in a rapid review are less likely to introduce unacceptable levels of uncertainty while still producing a product that remains useful to policy-makers. METHODS: This paper summarizes current research describing commonly used methods and practices that are used to conduct rapid reviews and presents key considerations and options to guide methodological choices for a rapid review. RESULTS: The most important step for a rapid review is for an experienced research team to have early and ongoing engagement with the people who have requested the review. A clear research protocol, derived from a needs assessment conducted with the requester, serves to focus the review, defines the scope of the rapid review, and guides all subsequent steps. Common recommendations for rapid review methods include tailoring the literature search in terms of databases, dates, and languages. Researchers can consider using a staged search to locate high-quality systematic reviews and then subsequently published primary studies. The approaches used for study screening and selection, data extraction, and risk-of-bias assessment should be tailored to the topic, researcher experience, and available resources. Many rapid reviews use a single reviewer for study selection, risk-of-bias assessment, or data abstraction, sometimes with partial or full verification by a second reviewer. Rapid reviews usually use a descriptive synthesis method rather than quantitative meta-analysis. Use of brief report templates and standardized production methods helps to speed final report publication. CONCLUSIONS: Researchers conducting rapid reviews need to make transparent methodological choices, informed by stakeholder input, to ensure that rapid reviews meet their intended purpose. Transparency is critical because it is unclear how or how much streamlined methods can bias the conclusions of reviews. There are not yet internationally accepted standards for conducting or reporting rapid reviews. Thus, this article proposes interim guidance for researchers who are increasingly employing these methods.
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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 | 0.343 | 0.683 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.011 | 0.011 |
| Bibliometrics | 0.016 | 0.012 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.031 | 0.021 |
| Open science | 0.007 | 0.013 |
| Research integrity | 0.015 | 0.013 |
| Insufficient payload (model declined to judge) | 0.241 | 0.218 |
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".