Olfactory Outcomes With Dupilumab in Chronic Rhinosinusitis With Nasal Polyps
Bibliographic record
Abstract
BACKGROUND: Loss of smell (LoS) is one of the most troublesome and difficult-to-treat symptoms of severe chronic rhinosinusitis with nasal polyps (CRSwNP). OBJECTIVE: To assess the impact of dupilumab on sense of smell in severe CRSwNP. METHODS: In the randomized SINUS-24 and SINUS-52 studies, adults with severe CRSwNP received dupilumab 300 mg subcutaneously or matching placebo every 2 weeks for 24 or 52 weeks, respectively. Smell was assessed using daily patient-reported LoS score (0-3) and University of Pennsylvania Smell Identification Test (UPSIT; 0-40). Data from the 2 studies were pooled through week 24. Relationships between patient phenotypes and smell outcomes were also assessed. RESULTS: We randomized 724 patients (286 placebo, 438 dupilumab); mean CRSwNP duration was 11 years; 63% had prior sinonasal surgery. Mean baseline LoS was 2.74. Dupilumab produced rapid improvement in LoS, evident by day 3, which improved progressively throughout the study periods (least squares mean difference vs placebo -0.07 [95% CI -0.12 to -0.02]; nominal P < .05 at day 3, and -1.04 [-1.17 to -0.91]; P < .0001 at week 24). Dupilumab improved mean UPSIT by 10.54 (least squares mean difference vs placebo 10.57 [9.40-11.74]; P < .0001) at week 24 from baseline (score 13.90). Improvements were unaffected by CRSwNP duration, prior sinonasal surgery, or comorbid asthma and/or nonsteroidal anti-inflammatory drug-exacerbated respiratory disease. Baseline olfaction scores correlated with all measured local and systemic type 2 inflammatory markers except serum total immunoglobulin E. CONCLUSIONS: Dupilumab produced rapid and sustained improvement in sense of smell, alleviating a cardinal symptom of severe CRSwNP.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".