Evaluation of esophageal motor function in clinical practice
Bibliographic record
Abstract
Esophageal motor function is highly coordinated between central and enteric nervous systems and the esophageal musculature, which consists of proximal skeletal and distal smooth muscle in three functional regions, the upper and lower esophageal sphincters, and the esophageal body. While upper endoscopy is useful in evaluating for structural disorders of the esophagus, barium esophagography, radionuclide transit studies, and esophageal intraluminal impedance evaluate esophageal transit and partially assess motor function. However, esophageal manometry is the test of choice for the evaluation of esophageal motor function. In recent years, high-resolution manometry (HRM) has streamlined the process of acquisition and display of esophageal pressure data, while uncovering hitherto unrecognized esophageal physiologic mechanisms and pathophysiologic patterns. New algorithms have been devised for analysis and reporting of esophageal pressure topography from HRM. The clinical value of HRM extends to the pediatric population, and complements preoperative evaluation prior to foregut surgery. Provocative maneuvers during HRM may add to the assessment of esophageal motor function. The addition of impedance to HRM provides bolus transit data, but impact on clinical management remains unclear. Emerging techniques such as 3-D HRM and impedance planimetry show promise in the assessment of esophageal sphincter function and esophageal biomechanics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".