Virtual reality simulation in endoscopy training: Current evidence and future directions
Bibliographic record
Abstract
Virtual reality simulation is becoming the standard when beginning endoscopic training. It offers various benefits including learning in a low-stakes environment, improvement of patient safety and optimization of valuable endoscopy time. This is a review of the evidence surrounding virtual reality simulation and its efficacy in teaching endoscopic techniques. There have been 21 randomized controlled trials (RCTs) that have investigated virtual reality simulation as a teaching tool in endoscopy. 10 RCTs studied virtual reality in colonoscopy, 3 in flexible sigmoidoscopy, 5 in esophagogastroduodenoscopy, and 3 in endoscopic retrograde cholangiopancreatography. RCTs reported many outcomes including distance advanced in colonoscopy, comprehensive assessment of technical and non-technical skills, and patient comfort. Generally, these RCTs reveal that trainees with virtual reality simulation based learning improve in all of these areas in the beginning of the learning process. Virtual reality simulation was not effective as a replacement of conventional teaching methods. Additionally, feedback was shown to be an essential part of the learning process. Overall, virtual reality endoscopic simulation is emerging as a necessary augment to conventional learning given the ever increasing importance of patient safety and increasingly valuable endoscopy time; although work is still needed to study the nuances surrounding its integration into curriculum.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".