Cough in pulmonary rehabilitation: a retrospective analysis of responders and nonresponders
Bibliographic record
Abstract
Background Pulmonary rehabilitation (PR) is essential for people with chronic respiratory diseases (CRDs), yet its impact on cough-related quality of life (CR-QoL) remains unexplored. We assessed the effects of PR on CR-QoL, described the characteristics of responders and nonresponders to PR, and explored determinants of responsiveness in this health domain in individuals with CRDs. Methods A retrospective study was conducted. We assessed CR-QoL using the Leicester Cough Questionnaire (LCQ) and the impact of the disease with the COPD Assessment Test (CAT), before and after PR. Cut-offs of <17.05 in LCQ total score and ≥10 in CAT were used to detect low CR-QoL and medium impact of the disease. Responders were defined as achieving a minimal clinically important difference (MCID) of ≥1.3 on the LCQ total score. Pre-versuspost-PR analysis involved the t-test, Wilcoxon test or McNemar test and comparisons between groups included the independent t-test, Mann–Whitney U-test or Fisher's exact test. Logistic regression was employed to investigate factors influencing MCID achievement. Results 135 participants with CRDs (39% females; age 68±10 years; 61% COPD; forced expiratory volume in 1 s (FEV1) % pred 62.6±23.0%) were included. After PR, significant improvements were observed in all LCQ domains and CAT. 31% of participants were identified as responders in the LCQ (36% females; age 66±10 years; 62% COPD; FEV1% pred 60.0±22.3%), showcasing significant differences in the LCQ and CAT compared to nonresponders. People with low CR-QoL and medium/high impact of the disease at baseline were 11 and 4 times more likely to respond to PR in CR-QoL, respectively. Conclusion PR enhances CR-QoL. Identification of CR-QoL and disease impact traits at baseline offers insights to optimise this outcome responsiveness to PR.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".