Learning curve for peroral endoscopic myotomy in therapeutic endoscopy experts and nonexperts: Large single‐center experience
Bibliographic record
Abstract
OBJECTIVES: Reports on learning curve for peroral endoscopic myotomy (POEM) in therapeutic endoscopy nonexperts are limited. We aimed to assess the number of cases required to achieve POEM proficiency for endoscopic submucosal dissection (ESD) experts and nonexperts. METHODS: This is a retrospective study at the largest POEM referral center in Japan. POEM between April 2014 and December 2020 were included. Nonexperts and ESD experts were divided by training phases: A, 1-20; B, 21-40; C, 41-60; D, 61-80; and E, 81-100 cases. Primary outcome was operation time, and the phase to reach target time (83 min) was investigated. Secondary outcomes were clinical success rate, adverse events, and post-POEM gastroesophageal reflux disease (GERD). RESULTS: Five hundred and sixty-six cases were performed by 14 nonexperts, and 555 cases by 15 ESD experts. As the primary outcome, operation time in nonexperts was: A, 95 (79-115.8); B, 86.5 (71-105); C, 80 (70-100); D, 73 (64.5-100.5); and E, 73.5 (57.8-88.8) min, while in ESD experts: A, 90 (74-128); B, 77 (70-92); and C, 77 (70-93.5) min (median [interquartile range]). Operation time decreased significantly as experience increased in both groups (P < 0.001), and nonexperts required 41-60 cases to achieve proficiency, while experts required 21-40 cases. As secondary outcomes, in nonexperts, clinical success was 96.9-100%, adverse events were 5.0-9.2%, symptomatic GERD was 11.8-26.5%, and proton pump inhibitor (PPI) intake was 11.5-18.7% in each phase. While in experts, clinical success was 96.2-100%, adverse events were 3.0-5.8%, symptomatic GERD was 14.6-22.0%, and PPI intake was 12.6-17.9%. There were no significant differences among training phases. CONCLUSIONS: Non-ESD experts require more cases to achieve proficiency in POEM. These results are useful for establishing POEM training programs and institutional implementation of the procedure.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".