Otoscopy simulation training in a classroom setting: A novel approach to teaching otoscopy to medical students
Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: To determine the effectiveness of using of an otoscopy stimulator to teach medical students the primary principles of otoscopy in large group training sessions and improve their confidence in making otologic diagnoses. STUDY DESIGN: Cross-sectional survey design. METHODS: In March 2013, the Department of Otolaryngology-Head and Neck Surgery held a large-scale otoscopy simulator teaching session at the MaRS Innovation Center for 92 first and second year University of Toronto medical students. Following the training session, students were provided with an optional electronic, nine-question survey related to their experience with learning otoscopy using the simulators alone, and in comparison to traditional methods of teaching. RESULTS: Thirty-four medical students completed the survey. Ninety-one percent of the respondents indicated that the overall quality of the event was either very good or excellent. A total of 71% of respondents either agreed, or strongly agreed, that the otoscopy simulator training session improved their confidence in diagnosing pathologies of the ear. The majority (70%) of students indicated that the training session had stimulated their interest in otolaryngology-head and neck surgery as a medical specialty. CONCLUSIONS: Organizing large-group otoscopy simulator training sessions is one method whereby students can become familiar with a wide variety of pathologies of the ear and improve both their diagnostic accuracy and their confidence in making otologic diagnoses. LEVEL OF EVIDENCE: NA
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
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".