Bibliographic record
Abstract
Patients with CC suffer significant reductions in quality of life, mental health and work productivity. In response, specialist cough clinics have emerged to provide structured, trait-based diagnosis and tailored management. This chapter outlines a comprehensive framework for establishing and running a dedicated cough clinic. Key components include pre-clinic triage, in-clinic assessments focused on treatable traits, structured follow-up incorporating virtual care, and coordination with primary and secondary care providers. Multidisciplinary collaboration can be helpful in complex cases, involving respiratory physicians, speech and language therapists, ENT, gastroenterology and mental health specialists. The chapter also discusses research integration, digital innovation, and strategies to enhance cost-effectiveness. Specialist cough clinics can also act as research hubs for trials and biomarker validation, while also serving as models of efficient, patient-centred care. Integration with digital tools and remote assessment further improves accessibility. With strategic planning, specialist cough clinics have the potential to reduce the burden of CC on patients and healthcare systems alike, while advancing the science and treatment of this complex condition. Cite as: Satia I, Hassan W, Marsden P. A practical guide to establishing a cough clinic. In: Song W-J, McGarvey L, Cho PSP, et al., eds. Chronic Cough (ERS Monograph). Sheffield, European Respiratory Society, 2025; pp. 146–154 [ https://doi.org/10.1183/2312508X.10027424 ].
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.194 | 0.176 |
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".