Efficacy outcomes in children with asthma receiving dupilumab plus medium-dose inhaled corticosteroids vs children who receive placebo and continue high-dose inhaled corticosteroids
Bibliographic record
Abstract
Background: Children receiving high-dose inhaled corticosteroids (hICS) may experience adverse effects. Dupilumab (DPL), a human monoclonal antibody that blocks interleukins 4 and 13 activity, reduced severe exacerbations and improved lung function in children with uncontrolled asthma and type 2 inflammation in the phase 3 VOYAGE study ( NCT02948959 ). Aims and objectives: The post hoc analysis evaluates efficacy of DPL plus medium-dose inhaled corticosteroids (mICS) vs placebo (PBO) with hICS continuation. Methods: Children with moderate-to-severe asthma and baseline blood eosinophil counts ≥150 cells/μL or FeNO ≥20 ppb received DPL 100/200 mg q2w plus mICS (DPL+mICS; n=134), or PBO q2w with hICS (PBO+hICS; n=50) for 52 weeks. Endpoints: adjusted annualized severe exacerbation rates, morning peak expiratory flow (AM PEF), and achievement of a 7-item Asthma Control Questionnaire (ACQ-7) score <1.5. Results: At Week 52, DPL+mICS significantly reduced exacerbations vs PBO+hICS by 74% (relative risk vs PBO+hICS [95% CI]: 0.257 [0.143, 0.463]; P<0.0001). DPL+mICS significantly improved AM PEF as early as Week 6; this effect persisted at Week 52 (LS mean difference vs PBO+hICS [95% CI]: 13.87 L/min [2.72, 25.02], P=0.0150 at Week 6; 20.75 L/min [2.86, 38.64], P=0.0232 at Week 52). At Week 52, more children achieved an ACQ-7 score <1.5 with DPL+mICS (85.1%) vs PBO+hICS (72.0%; OR vs PBO [95% CI]: 1.77 [0.75, 4.15]; P=0.1930). Conclusions: In children with moderate-to-severe asthma and type 2 inflammation, DPL+mICS reduced exacerbations and improved lung function and asthma control versus PBO+hICS continuation.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".