Evaluation of an otoscopy simulator to teach otoscopy and normative anatomy to first year medical students
Bibliographic record
Abstract
OBJECTIVE/HYPOTHESIS: Our study evaluates the effectiveness of the OtoSim as an educational tool for teaching otoscopy and normal middle ear anatomy to first-year medical students. STUDY DESIGN: Cross-sectional survey design. METHODS: A large group otoscopy simulator teaching session was held in January 2014 for 29 first-year medical students at the University of Toronto. Following the training session, survey questions were administered to assess the student experience. RESULTS: A total of 29 students completed the survey. All respondents rated the overall quality of the event as very good or excellent. Ninety-three percent of respondents indicated that the simulator increased their confidence in otoscopy. Students also commented that they were able to learn normal middle ear anatomy without causing discomfort to patients. CONCLUSIONS: The use of otoscopy simulation is a novel addition to traditional learning methods for undergraduate medical students. Students can effectively learn normal external and middle ear anatomy and improve their confidence in performing otoscopy examination. 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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".