Dupilumab reduced impact of severe exacerbations on lung function in patients with moderate‐to‐severe type 2 asthma
Bibliographic record
Abstract
Abstract Background Severe asthma exacerbations increase the risk of accelerated lung function decline. This analysis examined the effect of dupilumab on forced expiratory volume in 1 s (FEV1) in patients with moderate‐to‐severe asthma and elevated type 2 biomarkers from phase 3 LIBERTY ASTHMA QUEST (NCT02414854). Methods Changes from baseline in pre‐ and post‐bronchodilator (BD) FEV1 and 5‐item Asthma Control Questionnaire (ACQ‐5) scores were assessed in patients with elevated type 2 biomarkers at baseline (type 2–150/25: eosinophils ≥150 cells/μl and/or fractional exhaled nitric oxide [FeNO] ≥25 ppb; type 2–300/25: eosinophils ≥300 cells/μl and/or FeNO ≥25 ppb), stratified as exacerbators (≥1 severe exacerbation during the study) or non‐exacerbators. Results In exacerbators and non‐exacerbators, dupilumab increased pre‐BD FEV1 by Week 2 vs placebo; differences were maintained to Week 52 (type 2–150/25: LS mean difference (LSMD) vs placebo: 0.17 L (95% CI: 0.10–0.24) and 0.17 L (0.12–0.23); type 2–300/25: 0.22 L (0.13–0.30) and 0.21 L (0.15–0.28)), in exacerbators and non‐exacerbators, respectively (p < .0001). Similar trends were seen for post‐BD FEV1. Dupilumab vs placebo also showed significantly greater improvements in post‐BD FEV1 0–42 days after first severe exacerbation in type 2–150/25 (LSMD vs placebo: 0.13 L [0.06–0.20]; p = .006) and type 2–300/25 (0.14 L [0.06–0.22]; p = .001) patients. ACQ‐5 improvements were greater with dupilumab vs placebo in both groups. Conclusion Dupilumab treatment led to improvements in lung function independent of exacerbations and appeared to reduce the impact of exacerbations on lung function in patients who experienced a severe exacerbation during the study.
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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) | 0.000 | 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 | 0.000 | 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".