Assessment of dupilumab in children with moderate‐to‐severe type 2 asthma with or without evidence of allergic asthma
Bibliographic record
Abstract
Abstract Background Cytokines, such as interleukins (IL)‐4/5/13, play a key role in multiple type 2 inflammatory diseases, including allergic asthma. Dupilumab, a human monoclonal antibody, blocks the shared receptor component for IL‐4/IL‐13, inhibiting signaling. In this post hoc analysis of VOYAGE (NCT02948959), dupilumab efficacy was evaluated in patients aged 6–11 years with type 2 asthma with or without evidence of allergic asthma (baseline serum total IgE ≥30 IU/mL and ≥1 perennial aeroallergen‐specific IgE ≥0.35kU/L). Methods Annualized severe exacerbation rates (AER) and changes in pre‐bronchodilator (Pre‐BD) forced expiratory volume in one second (FEV1), percent‐predicted pre‐BD FEV1 (ppFEV1), and Asthma Control Score (ACQ)‐7 were assessed during the treatment period. Results 350 children (261 with and 89 without evidence of allergic asthma) were included. Dupilumab versus placebo significantly reduced AER in patients with (0.24 vs. 0.62, relative risk reduction [RRR]: 62% [95% CI, 39–76], P < .0001) and without (0.39 vs. 0.80, RRR: 51% [95% CI, 0–76], P < .05) evidence of allergic asthma. Significant improvements in ppFEV1, pre‐bronchodilator FEV1, and ACQ‐7 scores were observed in dupilumab versus placebo throughout the treatment period in patients with evidence of allergic asthma. In patients without evidence of allergic asthma, numerical improvements in pre‐bronchodilator FEV1 and asthma control were observed by Week 52. Conclusion Dupilumab versus placebo reduced asthma exacerbations in children with type 2 asthma irrespective of evidence of allergic asthma; similar trends were observed in changes in lung function. Significant improvement in asthma control was observed in patients with evidence of allergic asthma, but not in those without.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.001 | 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".