MétaCan
Menu
Back to cohort
Record W4312019861 · doi:10.1177/08465371221143247

A National Survey of Canadian Diagnostic Radiology Program Directors on the Transition to Competence by Design in Diagnostic Radiology Post-Graduate Medical Education

2022· article· en· W4312019861 on OpenAlexaffabout
H. Brian Bentley, Juvel Lee, Alanna Supersad, Hang Yu, Scott Wong, Matthew Stewart, Sri Sannihita Vatturi, Priscila Crivellaro, Aline D. Khatchikian, Cameron Hague, Jana Taylor, Linda Probyn

Bibliographic record

VenueCanadian Association of Radiologists Journal · 2022
Typearticle
Languageen
FieldMedicine
TopicRadiology practices and education
Canadian institutionsSt. Paul's HospitalMcGill UniversityUniversity of TorontoUniversity of ManitobaUniversity of SaskatchewanUniversity of AlbertaUniversity of OttawaUniversity of British Columbia
Fundersnot available
KeywordsMedicineCompetence (human resources)RadiologyGraduate medical educationPost graduateMedical physicsMedical educationFamily medicineManagementAccreditation

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)0.003
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.976
Threshold uncertainty score0.174

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.048
GPT teacher head0.320
Teacher spread0.272 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations2
Published2022
Admission routes2
Has abstractno

Explore more

Same venueCanadian Association of Radiologists JournalSame topicRadiology practices and educationFrench-language works237,207