Functional‐Structural Correlates in Achalasia: The Relationship of Esophageal Pressurization and Anatomy
Bibliographic record
Abstract
BACKGROUND AND AIMS: Achalasia subtypes are classified by high-resolution manometry (HRM) based on esophageal pressurization and contractility patterns, while esophagram-based classifications emphasize esophageal anatomy. We aimed to evaluate the relationship between esophageal pressurization on HRM and esophageal anatomy on esophagram among patients with untreated achalasia. METHODS: Adult patients with treatment-naïve achalasia that completed HRM and esophagram were included. HRM achalasia subtypes were determined by the Chicago Classification with pan-esophageal pressurization (PEP) measured among type I and type II achalasia. Anatomy on esophagram was assessed using the Brazilian (esophageal width) and Japanese Esophageal Society (JES; angulation/tortuosity) classifications. RESULTS: 222 patients, mean (SD) age 56 (16), 49% female were included. On HRM, 32% were type I, 53% were type II, and 15% were type III achalasia. Esophageal width and JES classification differed by HRM subtype (p-values < 0.001) with type I (HRM) having greatest esophageal width (median (IQR) 5.1(4.0-6.0) cm) and most JES-C 93% (14/15), while type III achalasia had the least (width 2.6 (2.0-3.0) cm) and 0 were JES-C. Among type I and II achalasia, higher esophageal width was significantly correlated with lower median PEP and fewer swallows exceeding PEP thresholds of 10, 15, 20, or 30 mmHg. CONCLUSIONS: HRM subtypes and PEP on HRM correlated with esophageal morphology defined on esophagram. However, imperfect concordance between HRM and esophagram classifications suggests complementary value to assess achalasia disease stages related to disease chronicity and esophageal wall mechanics. Future investigations to facilitate combined assessment with HRM and esophagram may enhance achalasia phenotyping and treatment planning.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".