Development and initial validation of the McGill simulator for endoscopic sinus surgery (MSESS)
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 (VR) simulation has demonstrated benefit in many disciplines as an important educational tool for surgical training. Within the field of rhinology, there is a lack of ESS simulators with appropriate validity evidence supporting their integration into residency education. Objectives: The objectives of this study are: 1) to develop a new rhinology simulator, the McGill Simulator for ESS (MSESS); 2) to evaluate the acceptability, perceived realism and benefit of the MSESS among medical students, otolaryngology residents and faculty; and 3) to present evidence supporting its ability to differentiate users based on their level of training through performance metrics. Methods: The McGill Simulator for endoscopic sinus surgery (MSESS), a new sinus surgery VR simulator with haptic feedback, was developed with the National Research Council of Canada. 10 medical students, 10 junior residents, 10 senior residents and 3 expert sinus surgeons performed anterior ethmoidectomies, posterior ethmoidectomies and wide sphenoidotomies on the MSESS. Performance metrics related to quality (e.g. percentage of tissue removed), efficiency (e.g. time, path length, bimanual dexterity, etc.) and safety (e.g. contact with no-go zones, maximum applied force, etc.) were calculated. All users completed a post-simulation questionnaire related to the realism, usefulness and perceived benefits of training on the MSESS. 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 MSESS was found to be realistic and useful for training surgical skills with scores of 7.97 ± 0.29 and 8.57 ± 0.69, respectively on a 10-point Likert scale. Most students and residents (29/30) believed that it should be incorporated into their curriculum. There were significant differences between novice surgeons (medical students and junior residents) and senior surgeons (senior residents and sinus surgeons) in performance metrics related to quality (p<0.05), efficiency (p<0.01) and safety (p<0.05). Conclusion: The MSESS demonstrated initial evidence supporting its use for residency education. This simulator may be a potential resource to help fill the void in ESS training.
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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 | 0.006 | 0.011 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".