Examining the utility of a photorealistic virtual ear in otologic education
Bibliographic record
Abstract
BACKGROUND: Otolaryngology-head and neck surgical (OHNS) trainees' operating exposure is supplemented by a combination of didactic teaching, textbook reading, and cadaveric dissections. Conventional teaching, however, may not adequately equip trainees with an understanding of complex visuospatial relationships of the middle ear. Both face and content validation were assessed of a novel three-dimensional (3D) photorealistic virtual ear simulation tool underwent face and content validation as an educational tool for OHNS trainees. METHODS: A three-dimensional mesh reconstruction of open access imaging was generated using geometric modeling, which underwent global illumination, subsurface scattering, and texturing to create photorealistic virtual reality (VR) ear models were created from open access imaging and comiled into a educational platform. This was compiled into an educational VR platform which was explored to validate the face and content validity questionnaires in a prospective manner. OHNS post-graduate trainees were recruited from University of Toronto and University of Calgary OHNS programs. Participation was on a voluntary basis. RESULTS: Total of 23 OHNS post-graduate trainees from the two universities were included in this study. The mean comfort level of otologic anatomy was rated 4.8 (± 2.2) out of 10. Senior residents possessed more otologic surgical experience (P < 0.001) and higher average comfort when compared to junior residents [6.7 (± 0.7) vs. 3.6 (± 1.9); P = 0.001]. Face and content validities were achieved in all respective domains with no significant difference between the two groups. Overall, respondents believed OtoVIS was a useful tool to learn otologic anatomy with a median score of 10.0 (8.3-10.0) and strongly agreed that OtoVIS should be added to OHNS training with a score of 10.0 (9.3-10.0). CONCLUSIONS: OtoVIS achieved both face and content validity as a photorealistic VR otologic simulator for teaching otologic anatomy in the postgraduate setting. As an immersive learning tool, it may supplement trainees' understanding and residents endorsed its use.
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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 | 0.004 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".