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Record W4288684965 · doi:10.1186/s13643-022-02011-5

Paper 2: Performing rapid reviews

2022· review· en· W4288684965 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSystematic Reviews · 2022
Typereview
Languageen
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsCanadian Agency for Drugs and Technologies in HealthPublic Health Agency of Canada
FundersAlliance for Health Policy and Systems ResearchDepartment for International DevelopmentDepartment for International Development, UK GovernmentStyrelsen för Internationellt Utvecklingssamarbete
KeywordsMedicineTraditional medicine

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.655
metaresearch head score (Gemma)0.339
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Meta-epidemiology (broad), Scholarly communication, Open science, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch, Meta-epidemiology (narrow), Meta-epidemiology (broad), Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.696
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.6550.339
Meta-epidemiology (narrow)0.0040.001
Meta-epidemiology (broad)0.1260.051
Bibliometrics0.0020.008
Science and technology studies0.0010.000
Scholarly communication0.0030.001
Open science0.0150.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.2840.200

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.

Opus teacher head0.864
GPT teacher head0.570
Teacher spread0.294 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it