Virtual reality simulation training in endoscopy: a Cochrane review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Endoscopy programs are increasingly integrating simulation training. We conducted a systematic review to determine whether virtual reality (VR) simulation training can supplement and/or replace conventional patient-based endoscopy training for health professional trainees with limited or no prior endoscopic experience. METHODS: We searched medical, educational, and computer literature databases in July 2017 for trials that compared VR simulation training with no training, conventional training, another form of simulation training, or an alternative method of VR training. We screened, abstracted data, and performed quantitative analysis and quality assessment through Cochrane methodology. RESULTS: We included 18 trials with 3817 endoscopic procedures. VR training provided no advantage over no training or conventional training based on the primary outcome of composite score of competency. VR training was advantageous over no training based on independent procedure completion (relative risk [RR] = 1.62, 95 % confidence interval [CI] 1.15 - 2.26, moderate-quality evidence), overall rating of performance (mean difference [MD] 0.45, 95 %CI 0.15 - 0.75, very low-quality evidence), and mucosal visualization (MD 0.60, 95 %CI 0.20 - 1.00, very low-quality evidence). Compared with conventional training, VR training resulted in fewer independent procedure completions (RR = 0.45, 95 %CI 0.27 - 0.74, low-quality evidence). We found no differences between VR training and no training or conventional training for other outcomes. Based on qualitative analysis, we found no significant differences between VR training and other forms of simulation training. VR curricula based in educational theory provided benefit with respect to composite score of competency, compared with unstructured curricula. CONCLUSIONS : VR simulation training is advantageous over no training and can supplement conventional endoscopy training. There is insufficient evidence that simulation training provides benefit over conventional training.
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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 | 0.010 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.027 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 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) | 0.006 | 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".