Computer-Based Virtual Reality Colonoscopy Simulation Improves Patient-Based Colonoscopy Performance
Bibliographic record
Abstract
BACKGROUND: Colonoscopy simulators that enable one to perform computer-based virtual colonoscopy now exist. However, data regarding the effectiveness of this virtual training are limited. OBJECTIVE: To determine whether virtual reality simulator training translates into improved patient-based colonoscopy performance. METHODS: The present study was a prospective controlled trial involving 18 residents between postgraduate years 2 and 4 with no previous colonoscopy experience. These residents were assigned to receive 16 h of virtual reality simulator training or no training. Both groups were evaluated on their first five patient-based colonoscopies. The primary outcome was the number of proctor 'assists' required per colonoscopy. Secondary outcomes included insertion time, depth of insertion, cecal intubation rate, proctor- and nurse-rated competence, and patient-rated pain. RESULTS: The simulator group required significantly fewer proctor assists than the control group (1.94 versus 3.43; P ≤ 0.001), inserted the colonoscope further unassisted (43 cm versus 24 cm; P=0.003) and there was a trend to intubate the cecum more often (26% versus 10%; P=0.06). The simulator group received higher ratings of competence from both the proctors (2.28 versus 1.88 of 5; P=0.02) and the endoscopy nurses (2.56 versus 2.05 of 5; P=0.001). There were no significant differences in proctor-, nurse- or patient-rated pain, or attention to discomfort. CONCLULSIONS: Computer-based colonoscopy simulation in the initial stages of training improved novice trainees' patient-based colonoscopy performance.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".