Cough predicts prognosis in idiopathic pulmonary fibrosis
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: The clinical associations and prognostic value of cough in IPF have not been adequately described. The objective of this study was to describe the characteristics and prognostic value of cough in IPF. METHODS: Subjects with IPF were identified from an ongoing longitudinal database. Cough and other clinical variables were recorded prospectively. Logistic regression was used to determine predictors of cough and predictors of disease progression, defined as 10% decline in FVC, 15% decline in DL(CO) , lung transplantation or death within 6 months of clinic visit. The relationship of cough with time to death or lung transplantation was analysed using Cox proportional hazards analysis. RESULTS: Two hundred and forty-two subjects were included. Cough was reported in 84% of subjects. On multivariate analysis, cough was less likely in previous smokers (OR 0.07, 95% CI: 0.01-0.55, P = 0.01), and more likely in subjects with exertional desaturation (OR 2.56, 95% CI: 1.15-5.72, P = 0.02) and lower FVC (OR 0.76, 95% CI: 0.60-0.96, P = 0.02). Cough predicted disease progression (OR 4.97, 95% CI: 1.25-19.80, P = 0.02) independent of disease severity, and may predict time to death or lung transplantation (HR 1.78, 95% CI: 0.94-3.35, P = 0.08). CONCLUSIONS: Cough in IPF is more prevalent in never-smokers and patients with more advanced disease. Cough is an independent predictor of disease progression and may predict time to death or lung transplantation.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".