DUPILUMAB REDUCES SEVERE EXACERBATION RATES AND TOTAL IGE LEVELS IN CHILDREN WITH TYPE 2 ASTHMA, IRRESPECTIVE OF TRANSIENT INCREASE IN EOSINOPHILS
Bibliographic record
Abstract
Background: Efficacy of dupilumab (DPL) in children (6–11 years) with type 2 asthma was reported in VOYAGE ( NCT02948959 ) and the EXCURSION ( NCT03560466 ) extension study. Aims and objectives: To evaluate efficacy of DPL in children with and without an early increase in eosinophils (<500 cells/µL at baseline AND ≥500 cells/µL at VOYAGE Week 12). Methods: VOYAGE regimen: add-on DPL 100/200 mg every two weeks or placebo (PBO) for 52 weeks; EXCURSION: DPL for 52 weeks (all children). Endpoints: severe exacerbation rates and change in total IgE. Results: DPL vs PBO significantly reduced severe exacerbation rates and maintained these reductions in EXCURSION, regardless of changes in eosinophils from baseline to Week 12 of VOYAGE (Figure). At Week 52 of VOYAGE, DPL reduced total IgE levels vs PBO in all children (median[Q1−Q3]: children with eosinophil changes, 30[−9 to 143] for PBO and −518[−1097 to −162] for DPL; children without eosinophil changes, 3[−64 to 110] for PBO and −369[−870 to −136] for DPL). Results were maintained through EXCURSION. Findings were similar when eosinophils were stratified by <2- or ≥2-fold change from baseline to Week 12. Conclusions: Dupilumab reduced exacerbation rates and total IgE levels for up to 2 years in children with type 2 asthma vs placebo, irrespective of increased eosinophil count from baseline to VOYAGE Week 12. erj;66/suppl_69/PA5890/F1 F1 F1
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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".