Dupilumab (DPL) Efficacy in Patients With Severe Chronic Rhinosinusitis with Nasal Polyps (CRSwNP) with/without Nonsteroidal Anti-inflammatory Drug-Exacerbated Respiratory Disease (NSAID-ERD): SINUS-24, SINUS-52 Trials
Bibliographic record
Abstract
Introduction: CRSwNP patients (pts) with NSAID-ERD often have more severe disease than non–NSAID‑ERD pts. DPL, a fully human mAb, blocks the shared receptor component for IL-4/IL-13, key drivers of type 2 inflammation. Aim: To assess DPL treatment effects in severe CRSwNP pts with/without NSAID-ERD on a background of intranasal mometasone furoate in pooled populations of phase 3 trials, SINUS-24 (NCT02912468) and SINUS‑52 (NCT02898454). Methods: CRSwNP pts with a medical history of NSAID-ERD were evaluated for endpoints of: nasal polyp score (NPS), nasal congestion (NC), disease severity (VAS), CT Lund–Mackay (CT-LMK) sinus opacification score, UPSIT smell test, total symptom score (TSS), daily loss of smell, SNOT-22. Results: DPL improved NPS, NC, VAS, CT-LMK, UPSIT, TSS, loss of smell, SNOT-22 and reduced the proportion of pts requiring SCS and/or surgery by 79.4/73.8% in pts with/without NSAID-ERD, respectively (all nominal P<0.0001 vs PBO; Table). Common AEs (in >5% pts) in the ITT population were nasopharyngitis, headache, worsening NP and asthma, epistaxis, and injection-site erythema, all occurring with higher frequency in PBO-treated pts. Conclusion: DPL improved endoscopic, clinical, radiologic and pt-reported outcomes similarly in CRSwNP pts with/without NASID-ERD and was well tolerated.
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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) | 0.001 | 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 | 0.001 | 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".