Management of patients after failed peroral endoscopic myotomy: a multicenter study
Bibliographic record
Abstract
Abstract Background Although peroral endoscopic myotomy (POEM) is highly effective for the management of achalasia, clinical failures may occur. The optimal management of patients who fail POEM is not well known. This study aimed to compare the outcomes of different management strategies in patients who had failed POEM. Methods This was an international multicenter retrospective study at 16 tertiary centers between January 2012 and November 2019. All patients who underwent POEM and experienced persistent or recurrent symptoms (Eckardt score > 3) were included. The primary outcome was to compare the rates of clinical success (Eckardt score ≤ 3) between different management strategies. Results 99 patients (50 men [50.5 %]; mean age 51.4 [standard deviation (SD) 16.2]) experienced clinical failure during the study period, with a mean (SD) Eckardt score of 5.4 (0.3). A total of 29 patients (32.2 %) were managed conservatively and 70 (71 %) underwent retreatment (repeat POEM 33 [33 %], pneumatic dilation 30 [30 %], and laparoscopic Heller myotomy (LHM) 7 [7.1 %]). During a median follow-up of 10 (interquartile range 3 – 20) months, clinical success was highest in patients who underwent repeat POEM (25 /33 [76 %]; mean [SD] Eckardt score 2.1 [2.1]), followed by pneumatic dilation (18/30 [60 %]; Eckardt score 2.8 [2.3]), and LHM (2/7 [29 %]; Eckardt score 4 [1.8]; P = 0.12). A total of 11 patients in the conservative group (37.9 %; mean Eckardt score 4 [1.8]) achieved clinical success. Conclusion This study comprehensively assessed an international cohort of patients who underwent management of failed POEM. Repeat POEM and pneumatic dilation achieved acceptable clinical success, with excellent safety profiles.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | medium |
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".