Minimum standards for training in colorectal endoscopic mucosal resection among advanced endoscopy trainees
Bibliographic record
Abstract
Background and study aims: Data on colorectal endoscopic mucosal resection (C-EMR) training during advanced endoscopy fellowship remain limited. We aimed to determine the number of procedures required by an "average" advanced endoscopy trainee (AET) to achieve competence in cognitive and technical C-EMR skills. Methods: AETs from advanced endoscopy training programs (AETPs) were graded on every C-EMR using a standardized assessment tool. Cumulative sum (CUSUM) analysis was used to generate individual and aggregate learning curves to estimate the minimum number of cases required to achieve competence for overall, technical, and cognitive components of C-EMR. AETs completed a self-assessment questionnaire on C-EMR competence at the end of their training. Results: A total of 22 AETs among 16 AETPs participated in this study. Nineteen AETs (86%) reported formal training in C-EMR with a mean number of 32 ± 22 cases prior to their AETP. In aggregate, 637 C-EMRs were performed (median of 32 per AET; interquartile range 17-45). Learning curve analyses revealed substantial variability in minimum volume of procedures needed to attain competence across different C-EMR skills (range: 19-39). A minimum of 19 cases were required to achieve overall competence using the global assessment score. All AETs reported feeling comfortable performing C-EMR independently at the end of AETP, yet only three (14%) achieved competence in their overall performance. Conclusions: The relatively low number of C-EMRs performed by many AETs may be insufficient to achieve competence. The estimated thresholds for an average AET to achieve competence in C-EMR provide a framework for AETPs in determining the minimal standards for case volume exposure during training.
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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 | 0.016 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".