A90 WHEN DO TRAINEES ACHIEVE COMPETENCY IN PERFORMING ENDOSCOPIC SUBMUCOSAL DISSECTION: A SYSTEMATIC REVIEW
Bibliographic record
Abstract
Abstract Background Endoscopic submucosal dissection (ESD) is an established organ sparing curative endoscopic resection technique for the management of pre-malignant and superficially invasive malignant lesions of the gastrointestinal tract. However, little is understood of its learning curve with suggested competency measures including en bloc resection, R0 resection, adverse events and resection speed. Aims We aimed to perform a systematic review on when competency is achieved in ESD. Methods Two authors independently searched MEDLINE and EMBASE (1946 to Aug 2021) for full-text original citations including grey literature assessing the ESD learning curve. A learning curve was defined as an assessment of competency as a function of increasing trainee experience. Thresholds for competency were defined as en bloc resection ≥ 80%, R0 resection ≥ 80%, perforation rate ≤ 5% and resection speed ≤ 6.67min/cm2. Results Forty-three studies (1 esophageal, 11 gastric, 27 colorectum, 4 multiple sites) with 157 trainees and 8780 ESD procedures were included. Baseline experience in esophagogastroduodenoscopy, colonoscopy, endoscopic mucosal resection and ESD were 800–10,000, 100–10,000, 4–700 and 0–300 procedures, respectively. 16 studies used animal model training prior to assessing the ESD learning curve. En bloc resection, R0 resection, perforation rate and resection speed were used as markers of competency in 33, 29, 28 and 21 studies, respectively. When pooling evaluations where competency was achieved, it was reached in the esophagus, stomach, colorectum and for multiple sites between <10, <10–150, <10–301, and <20–300 procedures, respectively (Table 1). However, competency in R0 resection, perforation rate, and resection speed was not uniformly achieved within 12 studies. Conclusions Competency in ESD can be achieved in <10 - 399 procedures, with variability dependent on organ site and baseline level of training. With the widespread adoption of ESD, standardization of training and the assessment of competency are needed. Funding Agencies None
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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 | 0.015 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".