Consensus goals and standards for specialist cough clinics: the NEUROCOUGH international Delphi study
Bibliographic record
Abstract
Background: Current guidelines on the management of chronic cough do not provide recommendations for the operation of specialist cough clinics. The objective of the present study was to develop expert consensus on goals and standard procedures for specialist cough clinics. Methods: We undertook a modified Delphi process, whereby initial statements proposed by experts were categorised and presented back to panellists over two ranking rounds using an 11-point Likert scale to identify consensus. Results: An international panel of 57 experts from 19 countries participated, with consensus reached on 15 out of 16 statements, covering the aims, roles and standard procedures of specialist cough clinics. Panellists agreed that specialist cough clinics offer optimal care for patients with chronic cough. They also agreed that history taking should enquire as to cough triggers, cough severity rating scales should be routinely used, and a minimum of chest radiography, spirometry and measurements of type 2 inflammatory markers should be undertaken in newly referred patients. The importance of specialist cough clinics in promoting clinical research and cough specialty training was acknowledged. Variability in healthcare resources and clinical needs between geographical regions was noted. Conclusions: The Delphi exercise provides a platform and guidance for both established cough clinics and those in planning stages.
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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 | 0.284 | 0.220 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".