Anatomy‐Specific Virtual Reality Simulation in Temporal Bone Dissection: Perceived Utility and Impact on Surgeon Confidence
Bibliographic record
Abstract
Objective To evaluate the effect of anatomy-specific virtual reality (VR) surgical rehearsal on surgeon confidence and temporal bone dissection performance. Study Design Prospective pre- and poststudy of a novel virtual surgical rehearsal platform. Setting Academic otolaryngology-head and neck surgery residency training programs. Subjects and Methods Sixteen otolaryngology-head and neck surgery residents from 2 North American training institutions were recruited. Surveys were administered to assess subjects' baseline confidence in performing 12 subtasks of cortical mastoidectomy with facial recess. A cadaver temporal bone was randomly assigned to each subject. Cadaver specimens were scanned with a clinical computed tomography protocol, allowing the creation of anatomy-specific models for use in a VR surgical rehearsal platform. Subjects then rehearsed a virtual mastoidectomy on data sets derived from their specimens. Surgical confidence surveys were administered again. Subjects then dissected assigned cadaver specimens, which were blindly graded with a modified Welling scale. A final survey assessed the perceived utility of rehearsal on dissection performance. Results Of 16 subjects, 14 (87.5%) reported a significant increase in overall confidence after conducting an anatomy-specific VR rehearsal. A significant correlation existed between perceived utility of rehearsal and confidence improvement. The effect of rehearsal on confidence was dependent on trainee experience and the inherent difficulty of the surgical subtask. Postrehearsal confidence correlated strongly with graded dissection performance. Subjects rated anatomy-specific rehearsal as having a moderate to high contribution to their dissection performance. Conclusion Anatomy-specific virtual rehearsal improves surgeon confidence in performing mastoid dissection, dependent on surgeon experience and task difficulty. The subjective confidence gained through rehearsal correlates positively with subsequent objective dissection performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 |
| 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".