Assessing cough symptom severity in refractory or unexplained chronic cough: findings from patient focus groups and an international expert panel
Bibliographic record
Abstract
Background: Cough symptom severity represents an important subjective end-point to assess the impact of therapies for patients with refractory or unexplained chronic cough (RCC/UCC). As existing instruments assessing the severity of cough are neither widely available nor tested for measurement properties, we aim to develop a new patient-reported outcome measure addressing cough severity. Objective: The aim of this study was to establish items and domains that would inform development of a new cough severity instrument. Methods: Three focus groups involving 16 adult patients with RCC/UCC provided data that we analysed using directed content analysis. Discussions led to consensus among an international panel of 15 experts on candidate items and domains to assess cough severity. Results: The patient focus group provided 48 unique items arranged under broad domains of urge-to-cough sensations and cough symptom. Feedback from expert panel members confirmed the appropriateness of items and domains, and provided an additional subdomain related to cough triggers. The final conceptual framework comprised 51 items in the following domains: urge-to-cough sensations (subdomains: frequency and intensity) and cough symptom (subdomains: triggers, control, frequency, fit/bout duration, intensity, quality and associated features/sequelae). Conclusions: Consensus findings from patients and international experts established domains of urge-to-cough and cough symptom with associated subdomains and relevant items. The results support item generation and content validity for a novel patient-reported outcome measure for use in health research and clinical practice.
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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 | 0.109 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".