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Record W4416606340 · doi:10.1002/cesm.70063

Responsible Integration of Artificial Intelligence in Rapid Reviews: A Position Statement From the Cochrane Rapid Reviews Methods Group

2025· article· en· W4416606340 on OpenAlexaff
Gerald Gartlehner, Barbara Nußbaumer-Streit, Candyce Hamel, Chantelle Garritty, Ursula Griebler, Valerie King, Declan Devane, Chris Kamel

Bibliographic record

VenueCochrane Evidence Synthesis and Methods · 2025
Typearticle
Languageen
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsPublic Health Agency of CanadaOttawa HospitalCanadian Agency for Drugs and Technologies in HealthUniversity of Ottawa
Fundersnot available
KeywordsSystematic reviewFlaggingWorkflowApplications of artificial intelligencePosition paperTroubleshootingQuality (philosophy)

Abstract

fetched live from OpenAlex

OpenAlex records an abstract for this work, but it could not be fetched just now.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.628
metaresearch head score (Gemma)0.791
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: Methods
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.372
Threshold uncertainty score0.459

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.6280.791
Meta-epidemiology (narrow)0.0040.008
Meta-epidemiology (broad)0.0130.016
Bibliometrics0.0180.023
Science and technology studies0.0080.019
Scholarly communication0.0370.038
Open science0.0210.025
Research integrity0.0820.075
Insufficient payload (model declined to judge)0.0190.033

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 head0.584
GPT teacher head0.601
Teacher spread0.017 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
DomainMethods
GenreCommentary

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

Citations4
Published2025
Admission routes1
Has abstractyes

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