Investigations and management of chronic cough: update from the European Respiratory Society Chronic Cough Taskforce 2020
Bibliographic record
Abstract
Chronic cough affects approximately 10% of the general population, is highest amongst people aged 50 to 60 years, and is twice as common in women than men. It is described to last 8 weeks or longer in adults and does not respond well to treatment with over-the-counter medications and those targeting potential associated conditions. This is a debilitating condition with physical, social, and psychological consequences. The purpose of this review was to highlight the key messages on the management of chronic cough from the task force commissioned by the European Respiratory Society. The assessment of patients with chronic cough should include a thorough detailed history and examination to identify potential causes. The impact and severity can be assessed in a clinic using questionnaires. Potential causes of the condition vary and include, for example, angiotensin‑converting enzyme inhibitors, smoking, asthma, nonasthmatic eosinophilic bronchitis, gastroesophageal reflux disease, and upper airways cough syndrome. In many patients, coughing is persistent despite optimum medical therapy of the underlying medical condition and is hence referred to as refractory chronic cough. In some cases, no cause can be found and the cough is classified as unexplained chronic cough. If treatment of any underlying disease is unsuccessful at controlling cough, then neuromodulatory treatment such as a low‑dose opioid, gabapentin, pregabalin or speech and language therapy may be considered. There is no licensed treatment for chronic cough, but a new class of treatment targeting the purinergic P2X3 receptor is currently in phase 2 and 3 of 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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".