S1252 Validation of a Novel Colonoscopy Simulator for Distinguishing Novice, Intermediate, and Expert Endoscopists
Bibliographic record
Abstract
Introduction: Simulation-based training and assessment has the potential to reduce risks to patient safety and discomfort. Previous studies demonstrated construct validity in both virtual and physical colonoscopy simulators by being able to distinguish between subjects based on their level of endoscopic experience. We aim to assess the internal structure validity of a novel colonoscopy simulator in being able to differentiate expertise levels and to assess how well the simulator’s tasks measure different aspects of colonoscopy skills. Methods: This is a multi-center study involving 1 United States tertiary academic hospital and 1 Canadian community teaching hospital. Novices (<50 colonoscopies), intermediates (50-500 colonoscopies), and experts (>500 colonoscopies or attending physicians) were recruited. Participants completed 1 easy and 1 advanced level on the colonoscopy simulator model. Metrics recorded on the simulator include elongation of sigmoid colon, compression of colon, reaching sigmoid junction, reaching appendix, and time to completion. Video recordings of participants were de-identified and rated by expert endoscopists using a modified version of the Joint Advisory Group Direct Observation of Procedural Skills (JAG DOPS) criteria. The primary outcome was comparison of colonoscopy simulator-generated metrics and modified JAGDOPS scores between participant groups. Results: Eleven novices, 9 intermediates and 11 experts were recruited for the study. There was a statistically significant difference in all colonoscopy simulator metrics between intermediates and experts when compared to novices (P < 0.001). Expert and intermediates had significantly higher mean modified JAGDOPS scores compared to novices (P < 0.001) at both easy and advanced level. Mean insertion time was significantly lower and withdrawal time was longer in intermediate and expert groups compared to novices (P < 0.001). Conclusion: This next generation colonoscopy simulator model has construct validity in that it distinguishes the performance level of novices from intermediates and experts. This may suggest a benefit to implementing simulation-based education in the early stages of gastroenterology training. Moreover, simulation-based training with this colonoscopy model prior to initiating real-world practice has the potential to improve patient safety and training outcomes.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".