1088 RANDOMIZED CONTROLLED TRIAL OF VIRTUAL REALITY AND HYBRID SIMULATION FOR ROBOTIC SURGICAL TRAINING
Bibliographic record
Abstract
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 ...
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.025 | 0.002 |
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".