Heartburn as a Marker of the Success of Acid Suppression Therapy in Chronic Cough
Bibliographic record
Abstract
PURPOSE: Gastro-oesophageal reflux disease (GORD) is commonly thought to play an important role in chronic cough and patients are often empirically treated with acid suppression therapy. We sought to investigate the response rate to acid suppression treatment in patients with and without heartburn attending two specialist cough clinics. METHODS: A retrospective review of 558 consecutive patients referred to two specialist cough clinics was performed (UK and USA). Patients who were treated with acid suppression were included and their documented response to treatment was collected. Binary logistic regression was used to ascertain the value of reported heartburn in predicting the response of chronic cough to acid suppression therapy. RESULTS: Of 558 consecutive referrals, 238 patients were excluded due to missing data or cough duration of < 8 weeks. The remaining 320 patients were predominantly female (76%), with mean age 61 yrs (± 13) and 96.8% non-smokers, with chronic cough for 36 (18-117) months. Of 72 patients with heartburn, 20 (28%) noted improvement in their cough with acid suppression, whereas of 248 without heartburn, only 35 (14%) responded. Patients reporting heartburn were 2.7 (95% C.I. 1.3-5.6) times more likely to respond to acid suppression therapy (p = 0.007). CONCLUSION: In specialist cough clinics, few patients report a response of their chronic cough to acid suppression therapy. Nonetheless, heartburn is a useful predictor substantially increasing the likelihood of benefit.
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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 | 0.008 |
| 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) | 0.001 | 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".