Nintedanib plus sildenafil in patients with idiopathic pulmonary fibrosis (IPF): the INSTAGE trial
Bibliographic record
Abstract
Introduction: Nintedanib, a tyrosine kinase inhibitor, is an approved treatment for IPF. Sildenafil, a phosphodiesterase-5 inhibitor, causes predominantly pulmonary vasodilation. Combined therapy with nintedanib and sildenafil may provide additional benefits in patients with IPF and advanced diffusion capacity impairment. INSTAGE (NCT02802345) is an ongoing randomised, double-blind, parallel-group trial of nintedanib plus sildenafil vs nintedanib alone in patients with IPF and advanced diffusion capacity impairment. Aim: To describe the baseline characteristics of patients participating in INSTAGE. Methods: Patients with a diagnosis of IPF within the last 6 years and diffusing capacity of the lungs for carbon monoxide (DLco) ≤35% predicted (corrected for haemoglobin) were randomised to receive nintedanib 150 mg bid plus sildenafil 20 mg tid or nintedanib 150 mg bid plus placebo for 24 weeks. The primary endpoint is the change from baseline in St George’s Respiratory Questionnaire (SGRQ) total score at week 12. Results: Recruitment for the INSTAGE trial is complete. A total of 273 patients have been treated. At baseline, mean (SD) age was 70.1 (8.2) years, 78.8% were male, 72.5% were white, 74.4% were ex-smokers and 56.8% had no echocardiographic signs indicative of right heart dysfunction. Mean (SD) FVC was 67.1 (19.1) % predicted, DLco was 25.7 (6.8) % predicted (corrected for haemoglobin) and SGRQ total score was 55.3 (18.2). Conclusion: The INSTAGE trial will reveal whether combining nintedanib and sildenafil, provides additional benefits in patients with IPF and advanced lung function impairment. Efficacy and safety results will be available for presentation at the ERS congress.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".