Virtual reality simulation in robot-assisted surgery: meta-analysis of skill transfer and predictability of skill
Bibliographic record
Abstract
BACKGROUND: The value of virtual reality (VR) simulators for robot-assisted surgery (RAS) for skill assessment and training of surgeons has not been established. This systematic review and meta-analysis aimed to identify evidence on transferability of surgical skills acquired on robotic VR simulators to the operating room and the predictive value of robotic VR simulator performance for intraoperative performance. METHODS: MEDLINE, Cochrane Central Register of Controlled Trials, and Web of Science were searched systematically. Risk of bias was assessed using the Medical Education Research Study Quality Instrument and the Newcastle-Ottawa Scale for Education. Correlation coefficients were chosen as effect measure and pooled using the inverse-variance weighting approach. A random-effects model was applied to estimate the summary effect. RESULTS: A total of 14 131 potential articles were identified; there were eight studies eligible for qualitative and three for quantitative analysis. Three of four studies demonstrated transfer of surgical skills from robotic VR simulators to the operating room measured by time and technical surgical performance. Two of three studies found significant positive correlations between robotic VR simulator performance and intraoperative technical surgical performance; quantitative analysis revealed a positive combined correlation (r = 0.67, 95 per cent c.i. 0.22 to 0.88). CONCLUSION: Technical surgical skills acquired through robotic VR simulator training can be transferred to the operating room, and operating room performance seems to be predictable by robotic VR simulator performance. VR training can therefore be justified before operating on patients.
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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 | 0.022 | 0.070 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.037 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".