High-Resolution Esophageal Manometry: A Time Motion Study
Bibliographic record
Abstract
INTRODUCTION: High-resolution manometry (HRM) of the esophagus is a new technique that provides a more precise assessment of esophageal motility than conventional techniques. Because HRM measures pressure events along the entire length of the esophagus simultaneously, clinical procedure time should be shorter because less catheter manipulation is required. According to manufacturer advertising, the new HRM system is more accurate and up to 50% faster than conventional methods. OBJECTIVE: To test the hypothesis that clinical testing with HRM requires less procedural time than a standard water perfusion (WP) method. METHODS: Forty-one consecutive patients were studied (20 underwent WP and 21 underwent HRM). Using time-motion analysis, the start and end times for each task associated with performing the study were recorded. Patient discomfort and study quality were also assessed by using five- and four-point qualitative scales, respectively. RESULTS: Total procedure time was reduced on average by 25.6% in the HRM group (from 41.8 minutes with WP to 30.7 minutes with HRM, P<0.05). There was no significant difference in the discomfort scores reported by the study subjects and no difference in study quality. CONCLUSIONS: HRM requires less time to complete than conventional manometry and should therefore shorten the wait-times of patients scheduled for esophageal manometry and have a significant impact on the cost of performing this commonly used clinical investigation.
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".