Dupilumab reduces systemic corticosteroid use and sinonasal surgery rate in CRSwNP
Bibliographic record
Abstract
BACKGROUND: Chronic rhinosinusitis with nasal polyps (CRSwNP) is a type 2 inflammatory disease with a high symptom burden and poor quality of life. Treatment options include recurrent surgeries and/or frequent systemic corticosteroids (SCS). Dupilumab, a fully human monoclonal antibody, blocks the shared receptor component for interleukin-4 and interleukin-13, key drivers of type 2-mediated inflammation. We report results of pooled analyses from 2 randomised, double-blind, placebo-controlled phase 3 studies (SINUS 24 [NCT02912468]; SINUS-52 [NCT02898454]) to evaluate dupilumab effect versus placebo in adults with CRSwNP with/without SCS use and sinonasal surgery. METHODOLOGY: SINUS-24 patients were randomised 1:1 to subcutaneous dupilumab 300 mg (n=143) or placebo (n=133) every 2 weeks (q2w) for 24 weeks. SINUS-52 patients were randomised 1:1:1 to 52 weeks of subcutaneous dupilumab 300 mg q2w (n=150), 24 weeks q2w followed by 28 weeks of dupilumab 300 mg every 4 weeks (n=145) or 52 weeks of placebo q2w (n=153). RESULTS: Dupilumab reduced the number of patients undergoing sinonasal surgery (82.6%), the need for in-study SCS use (73.9%), and SCS courses (75.3%). Significant improvements were observed with dupilumab vs placebo regardless of prior sinonasal surgery or SCS use in nasal polyp, nasal congestion, Lund-MacKay, and Sinonasal Outcome Test (22-items) scores, and the University of Pennsylvania Smell Identification Test. CONCLUSIONS: Dupilumab demonstrated significant improvements in disease signs and symptoms and reduced the need for sino-nasal surgery and SCS use versus placebo in patients with severe CRSwNP, regardless of SCS use in the previous 2 years, or prior sinonasal surgery.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".