Does training on robotic virtual reality simulators improve the post-training robotic surgical skills of surgeons? A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: The effectiveness of virtual reality (VR) simulation training for improving robotic surgical skills is not firmly established. This systematic review and meta-analysis aimed to evaluate the impact of VR simulator training on surgeons' robotic surgical performance in the operating room (OR). AIM: To synthesize evidence regarding the association between VR-based robotic surgery training and subsequent technical performance inside the OR. DATA SOURCES: A comprehensive literature search was conducted in PubMed, Scopus, Embase, and CINAHL databases, adhering to PRISMA 2020 guidelines. ELIGIBILITY CRITERIA AND STUDY SELECTION: Studies evaluating VR simulator training for robotic surgeons with subsequent performance assessment in the OR were included. Risk of bias assessment was performed using the Medical Education Research Study Quality Instrument and the modified Newcastle-Ottawa Scale for Education (NOS-E), along with funding source appraisal. DATA SYNTHESIS: A total of 294 records were screened, resulting in 13 studies (281 participants) included in the systematic review, and 4 studies (72 participants) suitable for meta-analysis. Metaanalysis was conducted using a random-effects model to pool correlation coefficients between preand post-training performance. RESULTS: Of the included studies, nine reported positive evidence, three found no evidence, and one found negative evidence regarding VR training's role. The meta-analysis of before-after studies revealed a significant positive correlation ( r = 0.717, P < 0.05) between simulation performance and intraoperative outcomes. LIMITATIONS: The small number of studies included in the meta-analysis and methodological heterogeneity may limit generalisability of results. CONCLUSIONS: VR simulator training is associated with improved robotic surgical performance in the OR. Incorporation of VR simulation into robotic surgical curricula is likely beneficial for skill acquisition and operative readiness.
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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 | 0.023 | 0.065 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.036 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".