Effects of Nintedanib on Quantitative Lung Fibrosis Score in Idiopathic Pulmonary Fibrosis
Bibliographic record
Abstract
BACKGROUND: Nintedanib slows disease progression in patients with Idiopathic Pulmonary Fibrosis (IPF) by reducing decline in Forced Vital Capacity (FVC). The effects of nintedanib on abnormalities on high-resolution computed tomography scans have not been previously studied. OBJECTIVE: We conducted a Phase IIIb trial to assess the effects of nintedanib on changes in Quantitative Lung Fibrosis (QLF) score and other measures of disease progression in patients with IPF. METHODS: 113 patients were randomized 1:1 to receive nintedanib 150 mg bid or placebo double-blind for ≥6 months, followed by open-label nintedanib. The primary endpoint was the relative change from baseline in QLF score (%) at month 6. Analyses were descriptive and exploratory. RESULTS: Adjusted mean relative changes from baseline in QLF score at month 6 were 11.4% in the nintedanib group (n=42) and 14.6% in the placebo group (n=45) (difference 3.2% [95% CI: -9.2, 15.6]). Adjusted mean absolute changes from baseline in QLF score at month 6 were 0.98% and 1.33% in these groups, respectively (difference 0.35% [95% CI: -1.27, 1.96]). Adjusted mean absolute changes from baseline in FVC at month 6 were -14.2 mL and -83.2 mL in the nintedanib (n=54) and placebo (n=54) groups, respectively (difference 69.0 mL [95% CI: -8.7, 146.8]). CONCLUSION: Exploratory data suggest that in patients with IPF, 6 months' treatment with nintedanib was associated with a numerically smaller degree of fibrotic change in the lungs and reduced FVC decline versus placebo. These data support previous findings that nintedanib slows the progression 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 | 0.002 | 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 | 0.001 |
| 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".