Association between dupilumab and repeated exacerbations of chronic obstructive pulmonary disease: BOREAS and NOTUS
Bibliographic record
Abstract
Background: Repeated exacerbations can accelerate the progression of COPD. Dupilumab is a human monoclonal antibody that blocks signaling of interleukin-4/13, key and central drivers in type 2 inflammation. In BOREAS and NOTUS, dupilumab reduced exacerbations and improved lung function in patients with COPD and type 2 inflammation. Safety was consistent with the known dupilumab safety profile. However, the impact of dupilumab on recurrent exacerbations is still unknown. Aims & objectives: To examine the association between dupilumab and recurrent moderate or severe exacerbations within 30 days of the first exacerbation in patients with COPD. Methods: Patients from BOREAS ( NCT03930732 ) and NOTUS ( NCT04456673 ) with COPD, moderate-to-severe airflow limitation, and type 2 inflammation (screening blood eosinophils ≥300 cells/µL) on triple therapy, received add-on dupilumab 300 mg q2w or placebo for 52 weeks. The frequency of repeat exacerbation within 30 days of the end of the index exacerbation was assessed in the pooled intention-to-treat population subgroup who experienced ≥2 exacerbations. Results: Of the patients receiving dupilumab who experienced ≥2 exacerbations (n=132), 16 (12.1%) had repeated moderate (15 [11.4%]) or severe (1 [0.8%]) exacerbations within 30 days of the end of the first exacerbation. Of the patients receiving placebo who had ≥2 exacerbations (n=190), 33 (17.4%) experienced repeated moderate (30 [15.8%]) or severe (3 [1.6%]) exacerbations within 30 days. Conclusion: Dupilumab reduced the number of recurrent moderate or severe exacerbations within 30 days in patients with COPD and type 2 inflammation with ≥2 exacerbations.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".