Development of the McGill Simulator for Endoscopic Sinus Surgery: A New High-fidelity Virtual Reality Simulator for Endoscopic Sinus Surgery
Bibliographic record
Abstract
BACKGROUND: The technical challenges of endoscopic sinus surgery (ESS) and the high risk of complications support the development of alternative modalities to train residents in these procedures. Virtual reality simulation is becoming a useful tool for training the skills necessary for minimally invasive surgery; however, there are currently no ESS virtual reality simulators available with valid evidence supporting their use in resident education. Our aim was to develop a new rhinology simulator, as well as to define potential performance metrics for trainee assessment. METHODS: The McGill simulator for endoscopic sinus surgery (MSESS), a new sinus surgery virtual reality simulator with haptic feedback, was developed (a collaboration between the McGill University Department of Otolaryngology-Head and Neck Surgery, the Montreal Neurologic Institute Simulation Lab, and the National Research Council of Canada). A panel of experts in education, performance assessment, rhinology, and skull base surgery convened to identify core technical abilities that would need to be taught by the simulator, as well as performance metrics to be developed and captured. RESULTS: The MSESS allows the user to perform basic sinus surgery skills, such as an ethmoidectomy and sphenoidotomy, through the use of endoscopic tools in a virtual nasal model. The performance metrics were developed by an expert panel and include measurements of safety, quality, and efficiency of the procedure. CONCLUSION: The MSESS incorporates novel technological advancements to create a realistic platform for trainees. To our knowledge, this is the first simulator to combine novel tools such as the endonasal wash and elaborate anatomic deformity with advanced performance metrics for ESS.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".