<scp>Four‐dimensional</scp> impedance manometry volume metrics for predicting abnormal bolus retention
Bibliographic record
Abstract
BACKGROUND: The objective measurement for esophageal bolus volume and bolus clearance could classify abnormal high-resolution manometry (HRM) beyond the current Chicago classification. We aimed to compare the novel four-dimensional impedance manometry (4D HRM) volume metrics with timed barium esophagram (TBE). METHODS: Adults with esophageal symptoms undergoing HRM and TBE were included. A custom-built program for 4D HRM analysis measured esophageal luminal cross-sectional area (CSA) from impedance and subsequently derived esophageal bolus volume and clearance. 4D HRM volume metrics included pre-swallow residual volume, maximal volume, retention volume, and clearance ratio defined as 1.0-retention volume divided by the maximal volume. An abnormal TBE was defined as a column height >5 cm at 1 min or 5 min. KEY RESULTS: A total of 95 patients (normal motility: 33%; ineffective esophageal motility: 12%; absent contractility: 10%; esophagogastric junction outflow obstruction: 30%; type I achalasia: 5%; type II achalasia: 12%) were categorized into normal TBE (58%), abnormal TBE at 1 min (17%), and abnormal TBE at 5 min (25%). The AUROC demonstrated that, among all 4D HRM volume metrics, the clearance ratio had the best performance in predicting abnormal TBE at 5 min (AUROC, 95% confidence interval: 0.89, 0.82-0.96), and exhibited a strong negative correlation with TBE at 5 min (r = -0.65; p < 0.001). CONCLUSIONS & INFERENCES: Novel 4D HRM volume metrics provide objective measurement of esophageal bolus volume and bolus clearance. The clearance ratio has a strong correlation with TBE and could potentially serve as a substitute for TBE to measure esophageal retention.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".