Unexplained or refractory chronic cough and mental health disorders: review of this relationship and response to pregabalin therapy
Bibliographic record
Abstract
Rationale: The relationship between unexplained or refractory chronic cough (URCC) and mental health disorders (MHD) is not well understood. Therefore, we aimed to review the clinical characteristics of URCC patients with and without anxiety and/or depression, as well as patient outcomes on pregabalin therapy. Methods: 50 consecutive URCC patients at Montfort Hospital (Canada) were assessed; half had a diagnosis of anxiety and/or depression. Patients were prescribed pregabalin 75 mg oral qhs for 4 weeks, followed by 75 mg bid. We reviewed demographic data, medical diagnoses, prior cigarette smoking, and cough duration. Leicester Cough Questionnaire (LCQ) was completed at baseline and after 3 months of therapy. T-test and chi-square test were performed for the comparison of clinical characteristics and responses to pregabalin. Results: 33 patients were female; mean age was 63 years. Mean BMI was 31. 28 subjects were categorized as unexplained cough and 22 refractory. 22 were ex-smokers. Mean cough duration was 54 months. Patients with MHD were younger (59 vs 67, p=0.02); no significant relationship was identified with gender, BMI, category or duration of cough, smoking history, or baseline LCQ total score (p>0.05). 28 subjects reported a LCQ total score improvement, evenly split between both groups. Among patients with MHD, responders to pregabalin were more likely to have refractory cough than unexplained (p=0.04). Conclusions: MHD are common in URCC patients, but most clinical characteristics were similar to patients without MHD. We identified having refractory cough versus unexplained as a predictor of response to pregabalin in patients with MHD.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".