Does brain natriuretic peptide (BNP) at baseline influence the effects of nintedanib plus sildenafil in patients with IPF?
Bibliographic record
Abstract
Introduction: In the INSTAGE trial in patients with IPF and DLco ≤35% predicted, nintedanib plus sildenafil was not associated with a significant benefit on SGRQ total score (primary endpoint) vs nintedanib alone. However, nintedanib plus sildenafil was associated with stabilisation in BNP, a marker of right ventricular strain, and reduced decline in FVC vs nintedanib alone. Aim: To assess whether baseline BNP influenced the effects of nintedanib plus sildenafil vs nintedanib alone. Methods: In post-hoc analyses, patients with baseline BNP ≤ vs > median were compared on changes from baseline in BNP at week 24 and in SGRQ total score and FVC at weeks 12 and 24; time to absolute FVC ≥5% predicted or death; and time to relative FVC decline ≥10% predicted or death. Results: At baseline, median BNP was 52 ng/L; 140 patients had BNP ≤52 ng/L and 133 had BNP >52 ng/L. All endpoints showed numerical benefits of nintedanib plus sildenafil vs nintedanib alone in both subgroups. Compared with patients with baseline BNP below the median, the combination provided a significantly greater benefit on BNP levels and a numerical benefit on FVC in patients with higher baseline BNP. Conclusions: In patients with IPF and severely impaired gas exchange, the benefit of nintedanib plus sildenafil vs nintedanib alone on changes in BNP and FVC seemed more pronounced in patients with baseline BNP above the median.
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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) | 0.000 | 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 | 0.001 | 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".