Description and Preliminary Evaluation of a Low-Cost Simulator for Training and Evaluation of Flexible Endoscopic Skills
Bibliographic record
Abstract
BACKGROUND: The Society of American Gastrointestinal and Endoscopic Surgeons developed the Fundamentals of Endoscopic Surgery (FES) to test the knowledge and skills required to perform flexible endoscopy. The program includes online didactic material to complement the written component, but does not have a practice component for the skills portion. The purpose of this study was to develop and pilot test low-cost models to train for the hands-on component of the FES examination. METHODS: Based on the deconstructed skills tested in FES, a low-cost simulator and metrics that model retroflexion, instrumentation and targeting, loop reduction, and mucosal evaluation were developed. The model is reuseable and requires a real endoscope and tower. Validity evidence was obtained by comparing performance between novice endoscopists (NEs) and experienced endoscopists (EEs). RESULTS: Six NEs and 6 EEs participated. In retroflexion, EEs and NEs scored (median [interquartile range]) 72.9 (67.1; 78.6) and 37.9 (25.7; 50.0; P = .004), respectively. In targeting, EEs scored 102.0 (75.0; 110.0) and NEs scored 50.0 (25.0; 50.0; P = .089). In navigation and loop reduction, EEs scored 189.0 (108.0; 267.0) and NEs scored 0.0 (0.0; 0.0; P = .004). In mucosal evaluation, EEs scored 133.3 (103.3; 150.0) and NEs scored 66.7 (50.0; 103.3; P = .015). The median global scores were 116.6 (109.6; 135.8) for EEs and 39.1 (29.1; 40.6; P = .004) for NEs. CONCLUSION: This pilot study provides preliminary validity evidence to support using these tasks to measure basic flexible endoscopic skills. Subsequent studies will examine the implementation of a proficiency curriculum using this model and its value as a training tool for flexible endoscopy, or to prepare for the FES exam.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".