Four-Dimensional Impedance Manometry in Esophageal Motility Disorders
Bibliographic record
Abstract
INTRODUCTION: Four-dimensional high-resolution impedance manometry (4D HRM) uses impedance to estimate esophageal luminal cross-sectional area and track nadir impedance to measure intrabolus pressure (IBP). We aimed to determine whether 4D HRM metrics could define abnormal esophagogastric junction (EGJ) opening between Chicago Classification version 4.0 motility disorders and compare 4D HRM with functional lumen imaging probe (FLIP) metrics. METHODS: Symptomatic adult patients who completed high-resolution impedance manometry and FLIP were included and compared with an asymptomatic control group. 4D HRM analysis used custom-built software to measure IBP, maximum EGJ diameter, and distensibility index on supine test swallows. 4D HRM metrics were compared with FLIP EGJ metrics. RESULTS: Ninety patients (31 normal motility, 16 ineffective esophageal manometry, 9 absent contractility, 8 conclusive EGJ outflow obstruction [EGJOO], 12 type I achalasia, 14 type II achalasia, 12 type III achalasia, and 34 asymptomatic controls) were included. Phase 2 and 3 IBP was higher in type II and III achalasia compared with controls and normal motility groups ( P < 0.03). Maximum EGJ diameter and EGJ-distensibility index in the conclusive EGJOO and achalasia groups were significantly lower than in controls and normal motility groups ( P < 0.03). 4D HRM identified 37 of 44 (84%) subjects with normal EGJ opening and 29 of 39 (74%) subjects with reduced EGJ opening on FLIP. DISCUSSION: 4D HRM metrics correlated with expected clinical observations across a spectrum of esophageal motility disorders and defined EGJ obstruction. 4D HRM metrics may have value in defining EGJ obstruction in equivocal cases related to EGJOO or absent peristalsis.
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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 | 0.002 |
| 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 | 0.001 |
| 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".