Circulating biomarkers in subjects with progressive pulmonary fibrosis: data from the INBUILD trial
Bibliographic record
Abstract
Background We investigated the prognostic potential of circulating biomarkers at baseline and the effects of nintedanib on changes in these biomarkers in subjects with progressive pulmonary fibrosis (PPF). Methods In the INBUILD trial, subjects with PPF received nintedanib (n=332) or placebo (n=331). Associations between biomarker levels at baseline and the rate of forced vital capacity (FVC) decline (mL·year −1 ) over 52 weeks, time to interstitial lung disease (ILD) progression (absolute decline in FVC % predicted ≥10%) or death over 52 weeks, time to first acute exacerbation or death over the whole trial, and time to death over the whole trial were assessed in the placebo group. Changes in adjusted mean levels of biomarkers in the nintedanib and placebo groups were assessed using linear mixed models for repeated measures. Biomarker data were log 2 transformed prior to analysis. Analyses were corrected for multiplicity. Results Baseline level of s-ICAM was significantly associated with rate of FVC decline and time to ILD progression or death over 52 weeks in the placebo group. No biomarker was significantly associated with time to first acute exacerbation or death or time to death. Decreases in Krebs von den Lungen-6 (KL-6), surfactant protein D (SP-D), CA-125 and CA19-9 were observed in subjects who received nintedanib versus placebo over 52 weeks. The largest decrease was in CA-125. In a mediation analysis, 16.4% of the effect of nintedanib on change in FVC at week 52 was attributed to the treatment-related decrease in CA-125 at week 12. Conclusions In subjects with PPF, nintedanib reduced circulating CA-125 and, to a lesser extent, other markers of epithelial dysfunction.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".