Non‐technical skills curriculum incorporating simulation‐based training improves performance in colonoscopy among novice endoscopists: Randomized controlled trial
Bibliographic record
Abstract
BACKGROUND AND AIMS: Non-technical skills (NTS), involving cognitive, social and interpersonal skills that complement technical skills, are important for the completion of safe and efficient procedures. We investigated the impact of a simulation-based curriculum with dedicated NTS training on novice endoscopists' performance of clinical colonoscopies. METHODS: A single-blinded randomized controlled trial was conducted at a single center. Novice endoscopists were randomized to a control curriculum or a NTS curriculum. The control curriculum involved a didactic session, virtual reality (VR) simulator colonoscopy training, and integrated scenario practice using a VR simulator, a standardized patient, and endoscopy nurse. Feedback and training were provided by experienced endoscopists. The NTS curriculum group received similar training that included a small-group session on NTS, feedback targeting NTS, and access to a self-reflective NTS checklist. The primary outcome was performance during two clinical colonoscopies, assessed using the Joint Advisory Group Direct Observation of Procedural Skills (JAG DOPS) tool. RESULTS: Thirty-nine participants completed the study. The NTS group (n = 21) had superior clinical performance during their first (P < 0.001) and second clinical colonoscopies (P < .0.001), compared to the control group (n = 18). The NTS group performed significantly better on the VR simulator (P < 0.05) and in the integrated scenario (P < 0.05). CONCLUSION: Our findings demonstrate that dedicated NTS training led to improved performance of clinical colonoscopies among novices.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".