Deliberate Practice on a Virtual Reality Laparoscopic Simulator Enhances the Quality of Surgical Technical Skills
Why this work is in the frame
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Bibliographic record
Abstract
INTRODUCTION: Virtual reality (VR) simulation provides unique training opportunities. This study evaluates whether the deliberate practice (DP) can be successfully applied to simulated laparoscopic cholecystectomy (LC) for enhancement of the quality of surgical skills. METHODS: Twenty-six inexperienced surgeons underwent a training program for LC on a VR simulator. Trainees were randomly allocated to 1 of 2 specific protocols of 10 sessions comprising a total of 20 LCs. For each session, the control group performed 2 LCs separated by 30 minutes of occupational activities; the DP group were assigned 30 minutes of DP activities in between 2 LCs. Each participant then performed 2 LCs on a cadaveric porcine model. Quantitative parameters were recorded from the simulator and a motion tracking device; qualitative assessment utilized validated rating scales. RESULTS: Twenty-two subjects completed training. Learning curves on the VR simulator were significant for time taken and number of movements in both groups. The DP group was slower from the third LC (1373 vs. 872 seconds, P = 0.022) and utilized more movements from the seventh (942 vs. 701, P = 0.033). Global rating scores improved significantly in both groups over repeated LCs. The DP group revealed higher scores than control from tenth (19.5 vs. 14, P = 0.014) until the twentieth LC (22 vs. 16, P = 0.003). On the porcine model, the DP group also achieved higher global rating scores (25.5 vs. 19.5, P = 0.002). CONCLUSIONS: VR training improved dexterity for both groups, and led to transfer of skill onto a porcine LC model. The DP group achieved higher quality, and demonstrated superior transfer onto real tissues.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it