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Record W4290769249 · doi:10.1136/bmj-2022-070849

Reporting guideline for overviews of reviews of healthcare interventions: development of the PRIOR statement

2022· article· en· W4290769249 on OpenAlexaff
Michelle Gates, Allison Gates, Dawid Pieper, Ricardo M. Fernandes, Andrea C. Tricco, David Moher, Sue Brennan, Tianjing Li, Michelle Pollock, Carole Lunny, Dino Sepúlveda, Joanne E. McKenzie, Shannon D. Scott, Karen A. Robinson, Katja Matthias, Konstantinos I. Bougioukas, Paolo Fusar‐Poli, Penny Whiting, Stephana J. Moss, Lisa Hartling

Bibliographic record

VenueBMJ · 2022
Typearticle
Languageen
FieldMedicine
TopicClinical practice guidelines implementation
Canadian institutionsUniversity of CalgaryOttawa HospitalInstitute of Health EconomicsUniversity of OttawaQueen's UniversityPublic Health OntarioUniversity of TorontoSt. Michael's HospitalUniversity of Alberta
FundersNational Eye Institute
KeywordsDelphi methodChecklistDelphiGuidelineMedical educationPsychological interventionSystematic reviewHealth careStakeholderPsychologyMedicineFamily medicineMEDLINENursingPublic relationsPolitical scienceComputer science

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)0.504
metaresearch head score (Gemma)0.649
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Reporting · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.496
Threshold uncertainty score0.611

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.5040.649
Meta-epidemiology (narrow)0.0060.011
Meta-epidemiology (broad)0.0130.026
Bibliometrics0.0300.025
Science and technology studies0.0050.006
Scholarly communication0.0170.014
Open science0.0150.016
Research integrity0.0170.021
Insufficient payload (model declined to judge)0.0090.013

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 head0.653
GPT teacher head0.629
Teacher spread0.025 · 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
DomainReporting
GenreMethods

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

Citations807
Published2022
Admission routes1
Has abstractyes

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