Peroral endoscopic myotomy is effective and safe in non-achalasia esophageal motility disorders: an international multicenter study
Bibliographic record
Abstract
Abstract Background and study aims The efficacy of per oral endoscopic myotomy (POEM) in non-achalasia esophageal motility disorders such as esophagogastric junction outflow obstruction (EGJOO), diffuse esophageal spasm (DES), and jackhammer esophagus (JE) has not been well demonstrated. The aim of this international multicenter study was to assess clinical outcomes of POEM in patients with non-achalasia disorders, namely DES, JE, and EGJOO, in a large cohort of patients. Patients and methods This was a retrospective study at 11 centers. Consecutive patients who underwent POEM for EGJOO, DES, or JE between 1/2014 and 9/2016 were included. Rates of technical success (completion of myotomy), clinical response (symptom improvement/Eckardt score ≤ 3), and adverse events (AEs, severity per ASGE lexicon) were ascertained. Results Fifty patients (56 % female; mean age 61.7 years) underwent POEM for EGJOO (n = 15), DES (n = 17), and JE (n = 18). The majority of patients (68 %) were treatment-naïve. Technical success was achieved in all patients with a mean procedural time of 88.4 ± 44.7 min. Mean total myotomy length was 15.1 ± 4.7 cm. Chest pain improved in 88.9 % of EGJOO and 87.0 % of DES/JE (P = 0.88). Clinical success was achieved in 93.3 % of EGJOO and in 84.9 % of DES/JE (P = 0.41) with a median follow-up of 195 and 272 days, respectively. Mean Eckardt score decreased from 6.2 to 1.0 in EGJOO (P < 0.001) and from 6.9 to 1.9 in DES/JE (P < 0.001). A total of 9 (18 %) AEs occurred and were rated as mild in 55.6 % and moderate in 44.4 %. Conclusion POEM is effective and safe in management of non-achalasia esophageal motility disorders, which include DES, JE, and EGJOO.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".