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Record W2415378581 · doi:10.1503/cjs.008514

Endoscopy training in Canadian general surgery residency programs

2015· article· en· W2415378581 on OpenAlexaffvenueabout
Nori Bradley, Amy Bazzerelli, Jenny Lim, Valerie Wu Chao Ying, Sarah N. Steigerwald, Matt Strickland

Bibliographic record

VenueCanadian Journal of Surgery · 2015
Typearticle
Languageen
FieldMedicine
TopicColorectal Cancer Screening and Detection
Canadian institutionsHamilton General HospitalMcMaster UniversityUniversity of ManitobaVancouver General HospitalToronto General HospitalDalhousie UniversityUniversity of TorontoUniversity of British ColumbiaUniversity of Ottawa
Fundersnot available
KeywordsMedicineCredentialingEndoscopyResidency trainingStandardizationMedical educationGeneral surgerySurgeryContinuing education

Abstract

fetched live from OpenAlex

Currently, general surgeons provide about 50% of endoscopy services across Canada and an even greater proportion outside large urban centres. It is essential that endoscopy remain a core component of general surgery practice and a core competency of general surgery residency training. The Canadian Association of General Surgeons Residents Committee supports the position that quality endoscopy training for all Canadian general surgery residents is in the best interest of the Canadian public. However, the means by which quality endoscopy training is achieved has not been defined at a national level. Endoscopy training in Canadian general surgery residency programs requires standardization across the country and improved measurement to ensure that competency and basic credentialing requirements are met.

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)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.998
Threshold uncertainty score0.223

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0130.001

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 head0.134
GPT teacher head0.291
Teacher spread0.156 · 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
DomainEvaluation
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

Citations6
Published2015
Admission routes3
Has abstractyes

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