Anticholinergic, anti‐depressant and other medication use is associated with clinically relevant oesophageal manometric abnormalities
Bibliographic record
Abstract
BACKGROUND: Medications can affect gastrointestinal tract motility. However, their effects on oesophageal motility in particular are often not as widely known or may be underestimated. AIM: To review the effect of existing medication use on high-resolution oesophageal manometry (HRM) in a 'real-world' setting. METHODS: Adult patients with upper gut symptoms and normal endoscopy or imaging who had HRM over a 22-month period were analysed. Achalasia and major disorders of peristalsis were excluded. All medications taken within 24 hours of the procedure were prospectively recorded and compared with HRM results, controlling for age, gender and proton pump inhibitor use. RESULTS: A total of 502 patients (323 female, mean age 51) were recruited. Of these, 41.2% had normal oesophageal HRM, while 41.4% had ineffective oesophageal motility (IOM) and 7.6% had oesophagogastric junction outflow obstruction (OGJOO). Serotonin/norepinephrine reuptake inhibitors (SNRI) and opioids were associated with significantly higher resting lower oesophageal sphincter pressure. Benzodiazepines and opioids were associated with elevated integrated relaxation pressure. SNRI and inhaled beta-agonists were associated with increased distal contractile index, whereas calcium channel blockers were associated with a lower distal contractile index. Odds ratio of being on anticholinergics was higher in IOM patients vs normal (3.6, CI 1.2-10.8). Odds ratio for anticholinergics, inhaled beta-agonists, anticonvulsants, SNRIs and opioids (trend) were all > 3 for OGJOO patients vs normal. CONCLUSION: Many medication classes are associated with abnormal HRM variables and diagnoses such as OGJOO and IOM; some of these associations are probably causal. These possible links should be taken into consideration during manometry interpretation.
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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 | 0.000 | 0.003 |
| 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".