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Record W2148913386 · doi:10.1186/s13643-015-0040-4

Advancing knowledge of rapid reviews: an analysis of results, conclusions and recommendations from published review articles examining rapid reviews

2015· article· en· W2148913386 on OpenAlexafffund
Robin Featherstone, Donna M Dryden, Michelle Foisy, Jeanne‐Marie Guise, Matthew D. Mitchell, Robin Paynter, Karen A. Robinson, Craig A. Umscheid, Lisa Hartling

Bibliographic record

VenueSystematic Reviews · 2015
Typearticle
Languageen
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsUniversity of Alberta
FundersU.S. Public Health ServiceUniversity of AlbertaAgency for Healthcare Research and QualityJohns Hopkins UniversityUniversity of Pennsylvania
KeywordsMedicineSystematic reviewTransparency (behavior)Scope (computer science)WorkgroupMEDLINEManagement scienceComputer science

Abstract

fetched live from OpenAlex

BACKGROUND: Rapid review (RR) products are inherently appealing as they are intended to be less time-consuming and resource-intensive than traditional systematic reviews (SRs); however, there is concern about the rigor of methods and reliability of results. In 2013 to 2014, a workgroup comprising representatives from the Agency for Healthcare Research and Quality's Evidence-based Practice Center Program conducted a formal evaluation of RRs. This paper summarizes results, conclusions, and recommendations from published review articles examining RRs. METHODS: A systematic literature search was conducted and publications were screened independently by two reviewers. Twelve review articles about RRs were identified. One investigator extracted data about RR methods and how they compared with standard SRs. A narrative summary is presented. RESULTS: A cross-comparison of review articles revealed the following: 1) ambiguous definitions of RRs, 2) varying timeframes to complete RRs ranging from 1 to 12 months, 3) limited scope of RR questions, and 4) significant heterogeneity between RR methods. CONCLUSIONS: RR definitions, methods, and applications vary substantially. Published review articles suggest that RRs should not be viewed as a substitute for a standard SR, although they have unique value for decision-makers. Recommendations for RR producers include transparency of methods used and the development of reporting standards.

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 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)0.473
metaresearch head score (Gemma)0.422
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Meta-epidemiology (broad), Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.340
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.4730.422
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0320.004
Bibliometrics0.0020.010
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0030.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.662
GPT teacher head0.517
Teacher spread0.144 · 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

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations142
Published2015
Admission routes2
Has abstractyes

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