Deliberate Practice on a Virtual Reality Laparoscopic Simulator Enhances the Quality of Surgical Technical Skills
Bibliographic record
Abstract
In Brief 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. Virtual reality (VR) simulation provides unique training opportunities. This study proves that deliberate practice (DP) can be successfully applied to simulated laparoscopic cholecystectomy (LC) for enhancement of the quality of surgical skills. 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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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".