Evaluation of a Web‐Based Module and an Otoscopy Simulator in Teaching Ear Disease
Bibliographic record
Abstract
Objective To determine which teaching method-otoscopy simulation (OS), web-based module (WM), or standard classroom instruction (SI)-produced the best improvement in the diagnosis of middle/external ear pathologies and the development of otoscopy clinical skills. Study Design Prospective randomized controlled nonclinical trial. Setting Preclerkship undergraduate medical education. Subjects and Methods Fifty-four medical students (first year, 26; second year, 28) were randomized to receive 1 of the 3 interventions: OS, WM, or SI. All students underwent baseline testing of diagnostic accuracy (25 ear pathologies) and otoscopy skills. Immediately following each intervention and 3 months later, testing was repeated. Results Baseline scores for diagnostic accuracy and otoscopy skills did not differ across intervention groups. Immediately postintervention, all groups showed an improvement in diagnostic accuracy ( P < .001). OS scored significantly higher than SI ( P < .001), as did WM ( P = .003). At 3-month follow-up, all groups continued to demonstrate improved diagnostic accuracy as compared with baseline. Again, OS showed improvement over SI ( P = .031). For otoscopy clinical skills, only OS improved immediately postintervention ( P < .001). OS had significantly higher scores than WM and SI ( P < .001). At 3-month follow-up, OS again showed improvement over WM ( P < .001) and SI ( P = .009). Conclusion All groups showed an improvement in diagnostic accuracy immediately postintervention, with the largest increases coming from OS and WM. Otoscopy clinical skills increased and were retained only in OS. Preclerkship medical student acquisition and retention of otolaryngology diagnostic skills can be greatly improved through web-based teaching modules and otoscopy simulation.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".