Effects of nintedanib on circulating biomarkers in patients with progressive pulmonary fibrosis: subgroups by fibrotic pattern on HRCT
Bibliographic record
Abstract
Introduction: Data from the INBUILD trial in subjects with progressive fibrosing ILDs other than IPF suggested that nintedanib may have effects on circulating biomarkers of epithelial injury. Aim: To investigate the effects of nintedanib on circulating biomarkers of epithelial injury in the INBUILD trial in subgroups by fibrotic pattern on HRCT. Methods: Fold changes from baseline in adjusted mean levels of biomarkers were analysed in subjects with a usual interstitial pneumonia (UIP)-like fibrotic pattern on HRCT or other fibrotic patterns on HRCT. Data were log10 transformed before analysis and estimates of change from baseline were back-transformed. Results: Of 663 subjects, 412 (62.1%) had a UIP-like fibrotic pattern on HRCT. Over 52 weeks, the effect of nintedanib on fold changes in CA-125 was consistent between subjects with a UIP-like fibrotic pattern and other fibrotic patterns on HRCT, whereas the effects of nintedanib on SP-D and CA19-9 were larger in subjects with a UIP-like pattern than other fibrotic patterns (Figure). Compared with placebo, nintedanib appeared to reduce KL-6 at week 52 in subjects with a UIP-like fibrotic pattern but not in subjects with other fibrotic patterns. Conclusions: The effect of nintedanib on reducing levels of CA-125 in subjects with progressive pulmonary fibrosis other than IPF did not differ between subgroups by fibrotic pattern on HRCT.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.001 |
| 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".