Current Pharmacological and Non-Pharmacological Therapies for Chronic Cough
Bibliographic record
Abstract
Chronic cough, defined as cough lasting 8 weeks or longer, affects approximately 10% of adults globally, but with large global variations with prevalence estimates ranging from 2–18%. The prevalence of chronic cough in adults over the age of 45 in the Canadian Longitudinal Study of Ageing (CLSA) was 16%, the second highest in the world. Interestingly, the prevalence and incidence is higher in English speaking compared with French speaking participants. Cough is the leading cause for ambulatory and primary care visits to physicians and one of the most common reasons for referral to specialist care. Chronic cough is associated with aging, smoking, higher body mass index, use of an ACE-inhibitor, and airways diseases. More recently, novel data has shown that symptoms of depression and psychological distress independently increase the risk of developing chronic cough by approximately 20%. Data from clinical trials and observational cohort studies suggest that patients with chronic cough have a median cough frequency of 20 coughs/hr. This may lead to distressing physical, psychological and social consequences such as urinary incontinence, exhaustion, fatigue, anxiety, frustration, embarrassment and social isolation, which all impairs quality of life. Chronic cough can be challenging to treat, since most over-the-counter therapies are ineffective and current treatments for chronic cough are all considered ‘off-label’. Although most cases are due to a benign cause, chronic cough can represent a serious underlying condition. A recent Canadian consensus has identified a simplified approach which can aid in the management of chronic cough and provide treatment for refractory or unexplained chronic cough. The guiding principles of this approach include i) investigation to rule out serious underlying conditions, ii) objective testing to prevent over and under-diagnosis, iii) treatment of identifiable diseases and traits and iv) monitoring to ensure effectiveness of treatment, including minimization of side effects and appropriate titration of treatment.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".