Acute exacerbations in patients with progressive pulmonary fibrosis
Bibliographic record
Abstract
Background Acute exacerbations of fibrosing interstitial lung diseases (ILDs) are associated with high mortality. We used prospective data from the INBUILD trial to investigate risk factors for acute exacerbations and the impact of these events in patients with progressive pulmonary fibrosis. Methods Patients with progressive fibrosing ILDs other than idiopathic pulmonary fibrosis (IPF) were randomised to receive nintedanib or placebo. Associations between baseline characteristics and time to first acute exacerbation were assessed using pooled data from both treatment groups using Cox proportional hazard models, firstly univariable models and then a multivariable model using forward stepwise selection. The risk of death was estimated based on the Kaplan−Meier method. Results Over a median follow-up of approximately 19 months, acute exacerbations were reported in 58 (8.7%) of 663 patients. In the risk factor analysis, the final model included diffusing capacity of the lung for carbon monoxide (DLCO) % predicted, treatment and age. LowerDLCO% predicted was associated with an increased risk of acute exacerbation with a hazard ratio (HR) of 1.56 (95% CI 1.21–2.02) per 10 units lower (p<0.001). Age ≥65 years was associated with a numerically increased risk (HR 1.55, 95% CI 0.87–2.77; p=0.14). Treatment with nintedanib conferred a numerically reduced riskversusplacebo (HR 0.60, 95% CI 0.35–1.02; p=0.06). The estimated risks of death ≤30 days and ≤90 days after an acute exacerbation were 19.0% (95% CI 8.9–29.2) and 32.0% (95% CI 19.7–44.2). Conclusions Acute exacerbations of progressive pulmonary fibrosis may have similar risk factors and prognostic impact as acute exacerbations of IPF.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".