1088 RANDOMIZED CONTROLLED TRIAL OF VIRTUAL REALITY AND HYBRID SIMULATION FOR ROBOTIC SURGICAL TRAINING
Notice bibliographique
Résumé
You have accessJournal of UrologyTechnology & Instruments: Robotics/Laparoscopy/Ureteroscopy III1 Apr 20101088 RANDOMIZED CONTROLLED TRIAL OF VIRTUAL REALITY AND HYBRID SIMULATION FOR ROBOTIC SURGICAL TRAINING Andrew Feifer, Adel Al-Almari, Evan Kovacs, Josee Delisle, Serge Carrier, and Maurice Anidjar Andrew FeiferAndrew Feifer New York, NY , Adel Al-AlmariAdel Al-Almari London, Canada , Evan KovacsEvan Kovacs Montreal, Canada , Josee DelisleJosee Delisle Montreal, Canada , Serge CarrierSerge Carrier Montreal, Canada , and Maurice AnidjarMaurice Anidjar Montreal, Canada View All Author Informationhttps://doi.org/10.1016/j.juro.2010.02.2285AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES The increasing utilization of robotic assisted surgery in urology has created new educational challenges regarding optimal training conditions for residents. While simulation has been incorporated into training for laparoscopy, it is unknown if simulation can play a preparatory role for the robotics platform. In a randomized fashion, we sought to investigate the optimal simulation environment for robotic surgery. METHODS We identified two widely validated laparoscopic simulation programs, LapSim® [LSM], and the McGill Inanimate System for Training and Evaluation of Laparoscopic Skills® (MISTELS) utilizing a hybrid augmented reality trainer, ProMIS® [PM]. Four tasks were used; peg transfer, intracorporeal suturing, cannulation, precision cutting. 20 surgically naive medical students were randomized to the practice sessions with either, both or none of these simulators. Baseline performance scores, training, and a final performance measurements were completed from February-May, 2009. Statistical performance changes were characterized using SAS®. Scores were compared using the Mann-Whitney U test. RESULTS All 20 medical students completed the preliminary performance analysis, five training sessions and the final performance analysis. Baseline performance characteristics amongst cohorts were statistically similar (á =0.05). On comparing mean scores differences between pre and post training sessions within each group, statistically significant performance enhancement in all four robotic tasks were identified in the groups receiving dual training (LSM and PM) [p<0.05]. Students trained on the PM or LSM alone did improve in cannulation alone, but did not demonstrate overall score or performance enhancement. Students without training did not illustrate performance improvement. CONCLUSIONS We have demonstrated that the use of ProMIS hybrid and LapSim VR simulators together leads to improvement in robotic task completion that exceeds what is seen with without simulation or with either simulator alone in novice medical students. Additionally, the use of MISTELS tasks can be adapted for the DaVinci® platform. Until pure robotic simulators are both validated and cost-effective, the utility of ProMIS and LapSim simulators for surgical readiness on the robotic platform cannot be understated. © 2010 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 183Issue 4SApril 2010Page: e423 Advertisement Copyright & Permissions© 2010 by American Urological Association Education and Research, Inc.MetricsAuthor Information Andrew Feifer New York, NY More articles by this author Adel Al-Almari London, Canada More articles by this author Evan Kovacs Montreal, Canada More articles by this author Josee Delisle Montreal, Canada More articles by this author Serge Carrier Montreal, Canada More articles by this author Maurice Anidjar Montreal, Canada More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...
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 | 0,005 | 0,012 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,004 | 0,004 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,005 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,025 | 0,002 |
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 ».