Temporal Bone Drilling Simulation Boot Camp Course
Bibliographic record
Abstract
Competency by design is changing the surgical landscape. Virtual reality simulation appears to be a promising training tool to assist in achieving surgical competency. This study was designed to determine if a boot camp style virtual reality (VR) mastoidectomy drilling course could be developed to improve a novice learner’s mastoidectomy drilling technique. Forty medical students were randomized to a traditional curriculum (control) group or a VR curriculum (intervention) group. Participants performed pre- and post-intervention knowledge testing, and mastoidectomy drilling sessions. Results of the study are an encouraging first step in demonstrating that a VR simulation boot camp course may improve a novice learners’: (i) understanding of the temporal bone anatomy as demonstrated by a significant difference between pre- and post-intervention knowledge testing (p < 0.01), (ii) drilling technique, as demonstrated by a significant difference between pre- and post-intervention drilling testing (p < 0.01), and (iii) ability to recognize dangerous or red flag areas in drilling a temporal bone. Future directions include a recommendation to implement a mastoidectomy VR simulation boot camp course at the annual Canadian Oto-HNS boot camp.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.003 |
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".