Learning curves in ERCP during advanced endoscopy training: a Canadian multicenter prospective study
Bibliographic record
Abstract
Abstract Background and study aims Growing emphasis on quality and patient safety has supported the shift toward competency-based medical education for advanced endoscopy trainees (AETs). In this study, we aimed to examine Canadian AETs learning curves and achievement of competence using an ERCP assessment tool with strong evidence of validity. Methods This prospective study was conducted at five institutions across Canada from 2017–2018. Data on every fifth procedure performed by trainees were collected using the United Kingdom Joint Advisory Joint Advisory Group of Gastrointestinal Endoscopy (JAG) ERCP Direct Observation of Procedural Skills (DOPS) tool, which includes a four-point rating scale for 27 items. Cumulative sum (CUSUM) analysis was used to create learning curves for overall supervision ratings and ERCP DOPS items by plotting scores for procedures performed during training. Results Eleven trainees who were evaluated for 261 procedures comprised our sample. The median number of evaluations by site was 49 (Interquartile range (IQR) 31–76) and by trainee was 15 (IQR 11–45). The overall cannulation rate by trainees was 82 % (241/261), and the native papilla cannulation rate was 78 % (149/191). All trainees achieved competence in the “overall supervision” domain of the ERCP DOPS by the end of their fellowship. Trainees achieved competency in all individual domains, except for tissue sampling and sphincteroplasty. Conclusions Canadian AETs are graduating from fellowship programs with acceptable levels of competence for overall ERCP performance and for the most specific tasks. Learning curves may help identify areas of deficiency that may require supplementary training, such as tissue sampling.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".