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Record W1976775042 · doi:10.1097/mlg.0b013e31818208e7

Undergraduate Otolaryngology Education in Canadian Medical Schools

2008· article· en· W1976775042 on OpenAlexaffabout
Paolo Campisi, Jamil Asaria, Dale Brown

Bibliographic record

VenueThe Laryngoscope · 2008
Typearticle
Languageen
FieldMedicine
TopicSurgical Simulation and Training
Canadian institutionsSickKids FoundationUniversity of Toronto
Fundersnot available
KeywordsOtorhinolaryngologyMedicineCurriculumMedical educationMedical schoolFamily medicineSurgeryPedagogyPsychology

Abstract

fetched live from OpenAlex

OBJECTIVES: To examine the quantity and nature of undergraduate otolaryngology instruction in the Canadian medical school system and to present the management of the undergraduate otolaryngology curriculum at the University of Toronto medical school with a yearly enrolment of 224 students. STUDY DESIGN: Survey questionnaire and narrative description. METHODS: A structured one-page survey was administered to the education directors of all 16 Canadian medical schools. The administration of core learning material, scheduling, patient encounter logging, and student and instructor evaluations with computerized, on-line systems at the University of Toronto was described. RESULTS: Rotations in otolaryngology were highly variable across medical schools. Mandatory rotations in otolaryngology were identified in only 6 of the 16 undergraduate curricula. The average length of clinical experience in schools with mandatory rotations was 4.6 days. CONCLUSIONS: The majority of Canadian medical graduates complete their undergraduate training with minimal clinical experience in otolaryngology. There is a clear discrepancy between the quantity and perceived need for training. To provide a thorough and equitable exposure to otolaryngology, a curriculum with standardized lectures and evaluation procedures is required.

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)0.008
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 score0.234

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.020
GPT teacher head0.306
Teacher spread0.286 · 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

Citations60
Published2008
Admission routes2
Has abstractyes

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