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Record W4205842149 · doi:10.1016/j.bja.2021.12.012

Validity evidence for the Anesthesia Clinical Encounter Assessment (ACEA) tool to support competency-based medical education

2022· article· en· W4205842149 on OpenAlexafffund

Bibliographic record

VenueBritish Journal of Anaesthesia · 2022
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsSickKids FoundationToronto General HospitalUniversity Health NetworkHospital for Sick ChildrenThe Wilson CentreSunnybrook Health Science Centre
FundersTemerty Faculty of Medicine, University of TorontoOntario Ministry of Research and InnovationNational Ethnic Affairs Commission of the People's Republic of ChinaUniversity of Toronto
KeywordsCompetence (human resources)AnesthesiologyPerioperativeValidityMEDLINECompetency assessmentPredictive validity

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.260
metaresearch head score (Gemma)0.667
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.260
Threshold uncertainty score0.913

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2600.667
Meta-epidemiology (narrow)0.0020.003
Meta-epidemiology (broad)0.0030.010
Bibliometrics0.0080.006
Science and technology studies0.0040.013
Scholarly communication0.0130.007
Open science0.0070.009
Research integrity0.0100.006
Insufficient payload (model declined to judge)0.0110.002

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 head0.067
GPT teacher head0.428
Teacher spread0.361 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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

Citations10
Published2022
Admission routes2
Has abstractno

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