Using a Virtual Reality Temporal Bone Simulator to Assess Otolaryngology Trainees
Bibliographic record
Abstract
OBJECTIVE: The objective of this study is to determine the feasibility of computerized evaluation of resident performance using hand motion analysis on a virtual reality temporal bone (VR TB) simulator. We hypothesized that both computerized analysis and expert ratings would discriminate the performance of novices from experienced trainees. We also hypothesized that performance on the virtual reality temporal bone simulator (VR TB) would differentiate based on previous drilling experience. STUDY DESIGN: The authors conducted a randomized, blind assessment study. METHODS: Nineteen volunteers from the Otolaryngology-Head and Neck Surgery training program at the University of Toronto drilled both a cadaveric TB and a simulated VR TB. Expert reviewers were asked to assess operative readiness of the trainee based on a blind video review of their performance. Computerized hand motion analysis of each participant's performance was conducted. RESULTS: Expert raters were able to discriminate novices from experienced trainees (P < .05) on cadaveric temporal bones, and there was a trend toward discrimination on VR TB performance. Hand motion analysis showed that experienced trainees had better movement economy than novices (P < .05) on the VR TB. CONCLUSION: Performance, as measured by hand motion analysis on the VR TB simulator, reflects trainees' previous drilling experience. This study suggests that otolaryngology trainees could accomplish initial temporal bone training on a VR TB simulator, which can provide feedback to the trainee, and may reduce the need for constant faculty supervision and evaluation.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".