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Record W2767662345 · doi:10.22374/cjgim.v12i3.163

Procedural Skills of a General Internist – Informed by the Front Line

2017· article· en· W2767662345 on OpenAlexaffvenueabout
Rodrigo B. Cavalcanti, Amy C Hendricks, Sharon E. Card

Bibliographic record

VenueCanadian Journal of General Internal Medicine · 2017
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsUniversity of SaskatchewanSt Martha's Regional HospitalUniversity of Toronto
Fundersnot available
KeywordsMedicineFront lineLine (geometry)Front (military)Family medicineLawMechanical engineeringGeometryEngineering

Abstract

fetched live from OpenAlex

Debate has occurred over many years regarding the ideal procedural skill set for a graduate of a General Internal Medicine (GIM) training program. A mixed methods study was used to establish a list of mandatory and selective procedural skills for all graduates of a 2-year PGY4/5 GIM subspecialty training program. This list was informed by previous literature, a survey of Canadian Society of Internal Medicine (CSIM) members, and a roundtable discussion at the October 2015 CSIM meeting in Prince Edward Island. The study illustrates the remarkable diversity of practice profiles and procedural skills performed by general internists in Canada.

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 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)0.002
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.039
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.018
GPT teacher head0.334
Teacher spread0.316 · 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 designNot applicable
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
Published2017
Admission routes3
Has abstractyes

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Same venueCanadian Journal of General Internal MedicineSame topicInnovations in Medical EducationFrench-language works237,207