Nintedanib in Asian patients with progressive fibrosing interstitial lung diseases: Results from the <scp>INBUILD</scp> trial
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: In the INBUILD trial in patients with progressive fibrosing interstitial lung diseases (ILDs), nintedanib reduced the rate of decline in forced vital capacity (FVC) with an adverse event profile characterized mainly by gastrointestinal events. We analysed the effects of nintedanib in the subset of Asian subjects. METHODS: Subjects with fibrosing ILDs other than idiopathic pulmonary fibrosis who had shown progression of ILD at any time within the prior 24 months despite management deemed appropriate in clinical practice were randomized to receive nintedanib or placebo. We analysed the rate of decline in FVC (ml/year) over 52 weeks in all Asian subjects and in Asian subjects with a usual interstitial pneumonia (UIP)-like fibrotic pattern on high-resolution computed tomography (HRCT). RESULTS: One hundred sixty-four subjects in the INBUILD trial were of Asian race. The rate of decline in FVC (ml/year) over 52 weeks in this subgroup was -116.8 in the nintedanib group and -207.9 in the placebo group (difference: 91.0 [95% CI: 8.1, 173.9]; nominal p = 0.03). In Asian subjects with a UIP-like fibrotic pattern on HRCT, the rate of decline in FVC (ml/year) over 52 weeks was -130.1 in the nintedanib group and -224.2 in the placebo group (difference: 94.1 [5.5, 182.7]; nominal p = 0.04). Adverse events led to treatment discontinuation in 19.0% of the nintedanib group and 13.8% of the placebo group. CONCLUSION: In Asian patients with progressive fibrosing ILDs, nintedanib reduced the rate of decline in FVC with adverse events that were manageable for most patients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".