Training in endoscopic mucosal resection: effectiveness and clinical utility of a short course for practicing endoscopists
Bibliographic record
Abstract
Abstract Background and Aims Endoscopic mucosal resection (EMR) is not systematically taught during most training programs. The aim of this study was to evaluate the effectiveness and clinical utility of a 1-day didactic and simulation-based EMR curriculum for practicing endoscopists without prior formal training in advanced endoscopic tissue resection. Methods We designed a 1-day lecture and simulation-based EMR course. Twelve participants completed the course. Effectiveness and clinical utility were evaluated using sequential explanatory mixed methods. All participants completed a pre-course multiple choice question (MCQ) examination followed by a separate, post-course MCQ examination with a similar blueprint. A survey was also conducted to assess cognitive fatigue, perceived benefit, and potential for change in EMR practice. Finally, a delayed MCQ examination was administered 10-14 weeks later to assess knowledge retention and qualitative data were sequentially collected from 3 candidates via semi-structured interviews. Results The mean pre-course score was 47.8% (SD 12.4%). The mean post-course score was 75% (9.9%) and the mean delayed score was 70.8% (13.6%), both significantly higher than the mean pre-course score (P < .001; Cohen’s d = 1.86 and P < .001; Cohen’s d = 1.47, respectively). There was no significant difference between the mean post- and delayed-course test scores (P = .2). Three themes emerged from the interviews: (1) a need for EMR training, (2) improved knowledge evaluating polyps, and (3) changed or refined EMR technique after the course. Conclusions This study demonstrates significant knowledge acquisition and retention of cognitive skills and suggests a change in practice following a 1-day focused didactic and simulation-based EMR course.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".