Randomized controlled trial of virtual reality and hybrid simulation for robotic surgical training
Bibliographic record
Abstract
OBJECTIVE: To evaluate if two commonly used laparoscopic simulators could be adapted and used successfully for the robotics platform in a laparoscopic and robotic naïve medical student population. MATERIALS AND METHODS: We identified two widely validated laparoscopic simulation programs, LapSim(®) (Surgical Science Sweden AB), and ProMIS(®) (Haptica, Ireland)for inclusion in the study. The McGill Inanimate System for Training and Evaluation of Laparoscopic Skills(®) task set was used for ProMIS, and adapted for the DaVinci(®) console (Intuitive Surgical, Inc., Sunnyvale, CA, USA) robotic platform. We then randomized 20 naïve medical students to receive training on either LapSim or ProMIS, both or neither, and evaluated them before and after training. RESULTS: When the groups were compared at baseline, there were no statistical differences in mean scores amongst the groups in univariate analysis (α= 0.05). When comparing mean scores within groups before and after training sessions, statistically significant performance enhancement in all four robotic tasks were identified in the groups receiving dual training. CONCLUSION: We have shown that the use of ProMIS hybrid and LapSim virtual reality (VR) simulators in conjunction with each other can considerable improve robotic console performance in novice medical students compared with hybrid and VR simulation alone.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| 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 | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 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".