Learning Curve of a Virtual Reality Simulator (Neurotouch) for Endoscopic Sinus Surgery
Bibliographic record
Abstract
Background Endoscopic sinus surgery is nuanced and technically challenging for novice trainees. With modern resident duty limitations and an increased focus on quality metrics in education and beyond, it is important to develop alternative tools for teaching this skill. Neurotouch is a validated high-fidelity virtual-reality simulator that provides haptic feedback and visual cues to simulate sinus surgery. Implementing the simulator in the era of competency-based curriculum has not been thoroughly investigated. In this study, we determined the learning curve for three tasks on the Neurotouch and assessed the Neurotouch as a learning tool compared with standard practice during cadaveric endoscopic sinus surgery. Methods Residents were randomized to virtual reality (VR) or control arms. Residents (PGY 1–4) in the VR arm completed seven to eight sessions on the Neurotouch. Each session consisted of two practice tasks (sphenoid endoscopy and polypectomy) and an evaluation task (endoscopic sinus surgery). Residents in the control arm did not have access to adjunctive tools. Participants were evaluated on performance metrics on quality, efficiency, and safety. They received immediate feedback following the simulation, displayed as a score out of 100 with points gained for successfully performing the task and points lost for errors. These scores were aggregated to calculate the learning curve for each of the tasks. After a washout period, residents in VR and control arms were evaluated during a cadaveric endoscopic sinus course. Results In the first task, the average time to completion of endoscopy for the first, third, and eighth attempts were 123.2 ± 41.7, 67.0 ± 49.2, and 36.8 ± 13.8 seconds respectively, with no significant change in overall score. There was significant improvement between the first and third ( p = 0.05) attempts, which was sustained during the eighth ( p = 0.001) attempt. The variance between trainees also narrowed with successive practice attempts. In the polypectomy task, there was also no significant difference between the average scores for the first, seventh, and eighth attempts. Evaluation task scores on first attempt, seventh, and eighth attempts were 28.6 ± 19.5, 68.8 ± 8.4, and 72.3 ± 8.9, respectively. The change from first to seventh and first to eighth attempt was 40 ± 20.4 ( p = 0.09) and 45 ± 12.9 ( p = 0.09). Conclusion In its current virtual reality iteration, there was a significant improvement in time to completion after three sessions, which was maintained through further attempts. The polypectomy task did not show a significant change in overall scoring. This may be due to the simplicity of the task and high average scores at first attempt. For the evaluation task, there was an increase in average score from first to last attempt, which approached but did not reach significance. This learning curve data will assist with implementing the Neurotouch as part of a simulation curriculum for novice trainees prior to spending time in the operating room. Further evaluation of the efficacy of the simulator in improving surgical skill and qualitative measures is pending.
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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 | 0.002 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".