Peroral endoscopic myotomy (POEM) for complex achalasia and the POEM difficulty score
Bibliographic record
Abstract
BACKGROUND: Peroral endoscopic myotomy (POEM) for achalasia is technically challenging to carry out in patients with type III, multiple prior treatments, prior myotomy, and sigmoid type. Herein, we present a series of consecutive patients with complex achalasia and introduce the POEM difficulty score (PDS). AIM: To demonstrate the application and discuss the utility of PDS and present the feasibility, safety, and efficacy of POEM in complex achalasia patients. METHODS: Forty consecutive POEM were carried out with 28 meeting the criteria for complex achalasia. Primary outcome was clinical success (Eckardt score ≤3) at a minimum of 3 months follow-up. Secondary outcomes included adverse events, procedural velocity and PDS. RESULTS: Twenty-eight complex and 12 non-complex POEM procedures were carried out with 100% and 92% clinical success, respectively, without any major adverse events with a median follow up of 15 months (complex) and 8 months (non-complex). Mean velocities for non-complex, type III, prior myotomy, ≥4 procedures and sigmoid type were as follows: 4.4 ± 1.6, 4.8 ± 1.5, 5.9 ± 2.2, 6.9 ± 2.2 and 8.2 ± 3.2 min/cm, respectively. Median PDS for non-complex, type III, prior myotomy, ≥4 treatments and sigmoid type were 1 (0-3), 2 (0-4), 2.5 (1-6), 3 (2-6) and 3.5 (1-6), respectively. PDS was shown to correlate well with procedural velocity with a correlation coefficient of 0.772 (Spearman's P < 0.001). CONCLUSIONS: PDS identifies the factors that contribute to challenging POEM procedures and correlates well with procedural velocity. The order of increasing difficulty of POEM in complex achalasia appears to be type III, prior myotomy, ≥4 treatments and sigmoid type.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".