Emerging drugs in the treatment of chronic cough
Bibliographic record
Abstract
INTRODUCTION: Chronic cough is a debilitating condition that is among the most common reasons for seeking medical attention yet remains challenging to manage. Identifying an underlying respiratory, nasal, or upper gastrointestinal disease triggering cough is the first step in assessment, but once this has been ruled out or adequately treated, many patients remain troubled with chronic cough. AREAS COVERED: This narrative review discusses the role of existing treatments and describes the current research landscape for the development of new therapies for chronic cough greater than 8 weeks that is refractory (RCC) or unexplained (UCC). The literature search includes published studies found on pubmed and conference abstracts until 2023. EXPERT OPINION: RCC/UCC can occur due to neuronal dysregulation of the vagus nerve or central nervous system. Hence, novel anti-tussives have targeted ion channels involved in the neuronal signaling which triggers cough. Although some therapies targeting receptors such as TRPV1 have failed to show efficacy, P2X3 antagonists have emerged as the most promising therapy for patients impacted by chronic cough. Disease-specific therapies such as for idiopathic pulmonary fibrosis are in early development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".