OC-105 Experience in polypectomy training and assessment: an international survey
Bibliographic record
Abstract
Introduction Colonoscopy is widely practised to reduce rates of colorectal cancer, although it does not confer absolute protection. The most hazardous part of colonoscopy is polypectomy, accounting for the majority of serious complications. It is unclear whether countries around the world have highlighted polypectomy as a specific skill that needs to be taught. The objective of the study was to assess both trainees’ and trainers’ experience of polypectomy training in countries around the world. Method Colonoscopy trainers from 19 countries worldwide (Figure 1)were asked to provide access to local trainers and trainees who would be invited to participate in a survey. An online survey was created asking about trainees’ experience of instruction and trainers’ experience of teaching polypectomy skills. Results Data were obtained from 610 colonoscopists- 348 (57.0%) trainers and 262 (43.0%) trainees. Most (79.6%) of the trainers surveyed were involved in polypectomy assessment weekly. 51.4% of those surveyed said that they used a specific framework when assessing polypectomy. 90.5% of trainees had a primary specialty of medical gastroenterology. The trainees had a breadth of colonoscopic experience, 31.7% having completed more than 500 colonoscopies and 38.2% fewer than 200 procedures. 51.1% stated that the principles of polypectomy had only been taught intermittently. Most (64.1%, 168 respondents) trainees had never been taught the principles of EMR. Only 53.1% of trainees had ever had their polypectomy technique formally assessed by any trainer. Of the 177 trainees who stated that they were competent at polypectomy, 70 (39.5%) had never had a formal evaluation of their polypectomy technique. Conclusion This study, the only in the literature, shows that polypectomy training is variable worldwide with low prevalence of formal competency assessment. There is a need to a) understand the learning curve for polypectomy, b) develop an international consensus defining optimal training methods and c) develop a framework of competency assessment. This should improve the safety of polypectomy and the effectiveness of colonoscopy in preventing colorectal cancer. Disclosure of interest None Declared. Reference The authors would like to acknowledge the contribution of all 610 respondents and in particular the local training faculty who facilitated this study
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".