Nintedanib in patients with idiopathic pulmonary fibrosis and preserved lung volume
Bibliographic record
Abstract
RATIONALE: There is no consensus as to when treatment for idiopathic pulmonary fibrosis (IPF) should be initiated. Some physicians prefer not to treat patients with preserved lung volume. OBJECTIVE: To investigate whether patients with IPF and preserved lung volume receive the same benefit from nintedanib as patients with more impaired lung volume. METHODS: Post hoc subgroup analyses of pooled data from the two replicate phase III INPULSIS trials by baseline FVC % predicted (≤90%, >90%). RESULTS: At baseline, 274 patients had FVC >90% predicted and 787 patients had FVC ≤90% predicted. In patients treated with placebo, the adjusted annual rate of decline in FVC was consistent between patients with FVC >90% predicted and FVC ≤90% predicted (-224.6 mL/year and -223.6 mL/year, respectively). There was no statistically significant difference between these subgroups in the effect of nintedanib on annual rate of decline in FVC, change from baseline in St George's Respiratory Questionnaire total score or time to first acute exacerbation. In patients with baseline FVC >90% predicted and ≤90% predicted, respectively, the adjusted annual rate of decline in FVC with nintedanib was -91.5 mL/year (difference vs placebo: 133.1 mL/year (95% CI 68.0 to 198.2)) and -121.5 mL/year (difference vs placebo: 102.1 mL/year (95% CI 61.9 to 142.3)). Adverse events associated with nintedanib were similar in both subgroups. CONCLUSIONS: Patients with IPF and preserved lung volume (FVC >90% predicted) have the same rate of FVC decline and receive the same benefit from nintedanib as patients with more impaired lung volume. TRIAL REGISTRATION NUMBER: NCT01335464 and NCT01335477.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| 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 | 0.001 | 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".