Otolaryngology in undergraduate medical education.
Bibliographic record
Abstract
OBJECTIVES: Otolaryngologic problems are common in primary care but are underrepresented in undergraduate medical education (UME). A significant portion of the total structured teaching received by physicians in primary care specialties is spent in UME, but teaching of otolaryngology in UME is limited and not standardized across Canadian medical schools. This study assessed the content and structure of otolaryngology UME at Canadian undergraduate medical programs and postgraduate primary care programs and surveyed community otolaryngologists to identify important clinical topics for inclusion in an otolaryngology curriculum. METHODS: A survey was created based on previously validated and published curriculum needs assessment instruments for medical education. Surveys were distributed to (1) undergraduate directors of otolaryngology, (2) postgraduate program directors of family and emergency medicine at English-language medical schools, and (3) community otolaryngologists in Canada. RESULTS: There is large variation in the quantity of otolaryngology teaching in UME. Knowledge of otolaryngology is formally evaluated at half of the responding programs, although skills in otolaryngology are rarely tested. Clerkship rotations are not uniformly offered, and the length of these placements is limited. Opportunities in postgraduate training for formal education in otolaryngology are rare. Several important topics that are not uniformly taught include sudden sensorineural hearing loss, sleep apnea, and nasal trauma. Important skills that are not uniformly taught include treatment of benign paroxysmal positional vertigo and epistaxis. CONCLUSIONS: There is limited time for teaching otolaryngology in UME; therefore, it is essential to emphasize teaching key topics and skills, which are highlighted in the results of this study.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.116 | 0.025 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".