Curriculum for training in endoscopic mucosal resection in the colon: European Society of Gastrointestinal Endoscopy (ESGE) Position Statement
Bibliographic record
Abstract
Endoscopic mucosal resection (EMR) is the standard of care for the complete removal of large (≥ 10 mm) nonpedunculated colorectal polyps (LNPCPs). Increased detection of LNPCPs owing to screening colonoscopy, plus high observed rates of incomplete resection and need for surgery call for a standardized approach to training in EMR. 1 : Trainees in EMR should have achieved basic competence in diagnostic colonoscopy, < 10-mm polypectomy, pedunculated polypectomy, and common methods of gastrointestinal endoscopic hemostasis. The role of formal training courses is emphasized. Training may then commence in vivo under the direct supervision of a trainer. 2 : Endoscopy units training endoscopists in EMR should have specific processes in place to support and facilitate training. 3: A trained EMR practitioner should have mastered theoretical knowledge including how to assess an LNPCP for risk of submucosal invasion, how to interpret the potential difficulty of a particular EMR procedure, how to decide whether to remove a particular LNPCP en bloc or piecemeal, whether the risks of electrosurgical energy can be avoided for a particular LNPCP, the different devices required for EMR, management of adverse events, and interpretation of reports provided by histopathologists. 4: Trained EMR practitioners should be familiar with the patient consent process for EMR. 5: The development of endoscopic non-technical skills (ENTS) and team interaction are important for trainees in EMR. 6: Differences in recommended technique exist between EMR performed with and without electrosurgical energy. Common to both is a standardized technique based upon dynamic injection, controlled and precise snare placement, safety checks prior to the application of tissue transection (cold snare) or electrosurgical energy (hot snare), and interpretation of the post-EMR resection defect. 7: A trained EMR practitioner must be able to manage adverse events associated with EMR including intraprocedural bleeding and perforation, and post-procedural bleeding. Delayed perforation should be avoided by correct interpretation of the post-EMR defect and treatment of deep mural injury. 8: A trained EMR practitioner must be able to communicate EMR procedural findings to patients and provide them with a plan in case of adverse events after discharge and a follow-up plan. 9: A trained EMR practitioner must be able to detect and interrogate a post-endoscopic resection scar for residual or recurrent adenoma and apply treatment if necessary. 10: Prior to independent practice, a minimum of 30 EMR procedures should be performed, culminating in a trainer-guided assessment of competency using a validated assessment tool, taking account of procedural difficulty (e. g. using the SMSA polyp score). 11: Trained practitioners should log their key performance indicators (KPIs) of polypectomy during independent practice. A guide for target KPIs is provided in this document.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.065 | 0.057 |
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".