Learning Curve of a Virtual Reality Simulator (Neurotouch) for Endoscopic Sinus Surgery
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,023 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».