A143 PREDICTIVE SCORES AND CLINICAL FAILURE AFTER PER-ORAL ENDOSCOPIC MYOTOMY
Bibliographic record
Abstract
Abstract Background Achalasia is a rare esophageal motility disorder characterized by incomplete lower esophageal sphincter relaxation and absent peristalsis. Increasingly, per-oral endoscopic myotomy (POEM) has been used as a first-line therapy for achalasia. However, a small proportion of patients will experience clinical failure after POEM (Eckhardt Score>3). Several small studies have attempted to predict which patients will experience failure after POEM; however, none have been validated outside their original centers. Aims To compare the performance of the Zhongshan, JAMS, and Urakami scores to predict key outcomes post-POEM. Methods A single centre retrospective cohort study from March 2016 to January 2024 including all adult patients undergoing POEM for achalasia with at least 3 months of follow-up was conducted. The performance of the different scores in predicting clinical failure and the presence of minimal symptoms (Ekhardt Score <=1) was analyzed. Scores were calculated and patients categorized as high-risk for failure (>=5% risk) or low risk based on score cutoffs from the original validation studies. Results 156 patients were included in our study. The median age of participants was 57.5 (IQR 36-68), and 70 were female (44.9%). 6 patients experienced clinical failure (3.8%) and 115 had minimal symptoms post-POEM (73.7%). The Urakami score demonstrated the highest sensitivity for predicting clinical failure (1.00; 95% CI 0.54-1.00) and presence of more than minimal symptoms (1.00; 95% CI 0.91-1.00), while the Zhongshan score exhibited the highest specificity for clinical failure (0.82; 95% CI 0.74-0.88) and more than minimal symptoms (0.84; 95% CI 0.76-0.90). (Table 1) Conclusions This study reports the first validation of predictive scores in POEM patients outside of their centres of origin. The Urakami score may play a role in identifying patients at low risk of clinical failure, while the Zhongshan score may help identify patients at high risk of clinical failure. Using these scores can aid in counseling patients pre-POEM about their expected outcomes. Test characteristics of various scores to predict outcomes after POEM 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".