Esophagogastric Junction pressure morphology: comparison between a station pull‐through and real‐time 3D‐<scp>HRM</scp> representation
Bibliographic record
Abstract
BACKGROUND: Esophagogastric junction (EGJ) competence is the fundamental defense against reflux making it of great clinical significance. However, characterizing EGJ competence with conventional manometric methodologies has been confounded by its anatomic and physiological complexity. Recent technological advances in miniaturization and electronics have led to the development of a novel device that may overcome these challenges. METHODS: Nine volunteer subjects were studied with a novel 3D-HRM device providing 7.5 mm axial and 45° radial pressure resolution within the EGJ. Real-time measurements were made at rest and compared to simulations of a conventional pull-through made with the same device. Moreover, 3D-HRM recordings were analyzed to differentiate contributing pressure signals within the EGJ attributable to lower esophageal sphincter (LES), diaphragm, and vasculature. KEY RESULTS: 3D-HRM recordings suggested that sphincter length assessed by a pull-through method greatly exaggerated the estimate of LES length by failing to discriminate among circumferential contractile pressure and asymmetric extrinsic pressure signals attributable to diaphragmatic and vascular structures. Real-time 3D EGJ recordings found that the dominant constituents of EGJ pressure at rest were attributable to the diaphragm. CONCLUSIONS & INFERENCES: 3D-HRM permits real-time recording of EGJ pressure morphology facilitating analysis of the EGJ constituents responsible for its function as a reflux barrier making it a promising tool in the study of GERD pathophysiology. The enhanced axial and radial recording resolution of the device should facilitate further studies to explore perturbations in the physiological constituents of EGJ pressure in health and disease.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".