Efficacy and Safety of Dupilumab (DUP) vs Omalizumab (OMA) in Chronic Rhinosinusitis with Nasal Polyps (CRSwNP) with Asthma (EVEREST study)
Bibliographic record
Abstract
Background: DUP and OMA are efficacious treatments for severe CRSwNP with asthma, but comparative efficacy studies are lacking. Methods: EVEREST ( NCT04998604 ) is the first head-to-head, randomised, double-blind, 24-week (W) phase 4 trial comparing efficacy and safety of DUP and OMA in adults with severe CRSwNP and coexisting asthma. Results: 360 patients (pts) randomised 1:1 (DUP n=181; OMA n=179); mean (SD) age 51.5 (13.11) years, 55% male, 49% with systemic corticosteroid use in past 2 years. Baseline characteristics were similar. At W24, least squares mean difference (LSMD) change from baseline (DUP vs OMA) in nasal polyp score (NPS, range 0‒8) and University of Pennsylvania Smell Identification Test (UPSIT, 0‒40) favoured DUP (NPS, ‒1.60 [SE 0.18], p<0.001; UPSIT, 8.0 [0.88], p<0.001). DUP also showed greater improvement at W24 in loss of smell score (0‒3; LSMD ‒0.81 [0.10], p<0.001), nasal congestion (0‒3; LSMD ‒0.58 [0.08], p<0.001), pre-bronchodilator FEV1 (litres; LSMD 0.15 [0.05], nominal p=0.003), 7-item Asthma Control Questionnaire (0‒6; LSMD ‒0.48 [0.09], nominal p<0.001) and 32-item asthma quality of life questionnaire (1‒7; LSMD 0.56 [0.10], nominal p<0.001). DUP and OMA safety profiles were comparable: 17.9% (DUP) vs 20.8% (OMA) pts experienced intervention-related treatment-emergent adverse events (TEAEs), and 1.7% (DUP) vs 4.0% (OMA) pts experienced serious TEAEs. Conclusion: DUP showed superiority over OMA in pts with severe CRSwNP and coexisting asthma in sinonasal and asthma outcomes, supporting its efficacy in type 2 respiratory diseases vs an active biologic comparator, and the known safety profiles of both therapies.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| 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 | 0.002 |
| 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".