Virtual reality simulation in endoscopy training: Current evidence and future directions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it