Effect of add-on dupilumab in elderly patients with chronic obstructive pulmonary disease: BOREAS and NOTUS
Bibliographic record
Abstract
Background: COPD is highly prevalent in elderly patients (≥65 years). Dupilumab, a human monoclonal antibody, blocks interleukin-4/-13, key and central drivers of type 2 inflammation. In BOREAS and NOTUS, dupilumab reduced exacerbations and improved lung function and quality of life in patients with COPD and type 2 inflammation. Safety was consistent with the known dupilumab safety profile. Aims & Objectives: To evaluate dupilumab efficacy in elderly patients with COPD and type 2 inflammation. Methods: In phase 3 BOREAS ( NCT03930732 ) and NOTUS ( NCT04456673 ), patients with COPD, moderate-to-severe airflow limitation, and type 2 inflammation (screening blood eosinophil counts ≥300 cells/µL) on triple therapy received add-on dupilumab 300 mg q2w or placebo for 52 weeks. Endpoints were adjusted annualized moderate or severe exacerbation rate, change from baseline in pre- and post-bronchodilator (BD) FEV1, St. George’s Respiratory Questionnaire (SGRQ, quality of life) and Evaluating Respiratory Symptoms in COPD (E-RS:COPD, symptom severity) total scores. Results: Dupilumab (n=550) vs placebo (n=522) reduced exacerbations by 28.1% (P<0.001) and improved lung function at Week 12 (least squares mean difference vs placebo [95% CI] pre-BD FEV1: 51 [17, 85] mL; P=0.003 and post-BD FEV1: 44 [9, 79] mL; P=0.014) and to Week 52 (pre- and post-BD FEV1 both: 52 [15, 89] mL; P=0.006). At Week 52, dupilumab vs placebo reduced SGRQ (−3.81 [−5.83, −1.79]; P<0.001) and E-RS:COPD (−1.19 [−1.85, −0.54]; P<0.001) total scores. Conclusions: In patients aged ≥65 years with COPD and type 2 inflammation, dupilumab reduced exacerbations and symptom severity, and improved lung function and quality of life.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".