Adding a radial dimension to the assessment of esophagogastric junction relaxation: validation studies of the 3D-eSleeve
Bibliographic record
Abstract
High-resolution manometry (HRM) with esophageal pressure topography (EPT) allowed for the establishment of an objective quantitative measurement of esophagogastric junction (EGJ) relaxation, the integrated relaxation pressure (IRP). This study assessed whether or not a novel 3D-HRM assembly could improve on this measurement. Twenty-five normal subjects were studied with both a standard HRM assembly and a novel hybrid assembly (3D-HRM), including a 9.0 cm 3D-HRM segment composed of 96 radially dispersed independent pressure sensors. The standard IRP was computed using each assembly and compared with a novel paradigm, the 3D-IRP, an analysis premised on finding the axial maximum and radial minimum pressure at each sensor ring along the sleeve segment. Fourteen additional subjects underwent barium swallows with 3D-HRM and concurrent videofluoroscopy to compare the electronic sleeve (eSleeve) paradigm (circumferential average) to the 3D eSleeve paradigm (radial minimum) as a predictor of transphincteric flow. The 3D-IRP was significantly less than all other calculations of IRP with the upper limit of normal being 12 mmHg vs. 17 mmHg for the standard IRP. The sensitivity (0.78) and the specificity (0.88) of the 3D-eSleeve were also better than the standard eSleeve (0.55 and 0.85, respectively) for predicting flow permissive time verified fluoroscopically. The 3D-IRP and 3D-eSleeve calculated using the radial pressure minimum lowered the normative range of EGJ relaxation (upper limit of normal 12 mmHg) and yielded intraluminal pressure gradients that better correlated with bolus flow than did analysis paradigms based on circumferentially averaged pressure.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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 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".