The Clinical Approach to Chronic Cough
Bibliographic record
Abstract
Chronic cough remains a significant clinical challenge, affecting approximately 10% of the population and leading to significant impairment in psychological, social, and physical quality of life. In recent years, efforts have intensified to elucidate the mechanisms underlying chronic cough and to focus on investigating and treating refractory chronic cough (RCC). A "treatable trait" approach, which focuses on identifying and addressing the specific associated causes of chronic cough, has gained traction. In some patients, RCC is likely driven by a neuropathic mechanism due to dysregulation of the neuronal pathways involved in the cough reflex, often clinically described as cough hypersensitivity syndrome. Although the initial treatment of underlying conditions remains central to managing treatable traits, the therapeutic options for RCC have expanded to include targeting cough hypersensitivity. First-line treatments now include neuromodulators and speech therapy with one P2X3 receptor antagonist (gefapixant) recently licensed in the European Union, United Kingdom, and Japan. Despite these advances, patient responses remain variable, underscoring the ongoing need for research into the pathophysiology and treatment of RCC. This article reviews current investigations and management options in treating chronic cough and RCC.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".