A47 VIRTUAL REALITY SIMULATION TRAINING IN GASTROINTESTINAL ENDOSCOPY: A COCHRANE REVIEW
Bibliographic record
Abstract
Abstract Background Training with virtual reality (VR) simulation is increasingly used for health professions training to allow novices to practice in a learner-centered, risk-free environment. This review was performed to evaluate the effectiveness of VR simulation training in gastrointestinal endoscopy. Aims To determine whether virtual reality simulation training can supplement and/or replace early conventional endoscopy training in diagnostic endoscopy for health professions trainees with limited or no prior endoscopic experience. Methods We searched 17 databases from inception until October 18, 2023. We included randomised and quasi-randomised 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 and abstracted data through Cochrane methodology. The primary outcome was composite score of competency. Secondary outcomes were procedure completion, procedure time, adverse events, patient discomfort, global rating of competency, and mucosal visualization. We calculated risk ratio for dichotomous outcomes and mean difference (MD) or standardised mean difference (SMD) for continuous outcomes with 95% CI. We used GRADE to assess the certainty of evidence. Results We included 20 trials (500 participants; 3975 endoscopic procedures). There was insufficient evidence to determine the effect of VR training on composite score of competency compared to no training or conventional training. VR training was advantageous over no training based for independent procedure completion (RR 1.62, 95% CI 1.15 to 2.26; moderate certainty evidence), overall rating of performance (mean difference [MD] 0.45, 95 %CI 0.15 - 0.75, very low certainty evidence), and mucosal visualization (MD 0.60, 95 %CI 0.20 - 1.00, very low certainty evidence). VR training resulted in fewer independent procedure completions compared to conventional training (RR = 0.45, 95 %CI 0.27 - 0.74, low certainty 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. 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 in endoscopy can supplement conventional endoscopy training and provides benefit compared to no training. Risk of bias summary Funding Agencies None
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".