Efficacy of dupilumab in patients with a history of prior sinus surgery for chronic rhinosinusitis with nasal polyps
Bibliographic record
Abstract
BACKGROUND: Chronic rhinosinusitis with nasal polyps (CRSwNP) is a type 2 inflammatory disease treated with sinus surgery when refractory to medical intervention. However, recurrence postsurgery is common. Dupilumab, a fully human monoclonal antibody, blocks the shared receptor for interleukin 4 (IL-4) and IL-13, key and central drivers of type 2 inflammation. We report the efficacy of dupilumab in patients with CRSwNP from the SINUS-24/SINUS-52 trials (NCT02912468/NCT02898454), by number of prior surgeries and time since last surgery. METHODS: Patients were randomized to placebo or dupilumab 300 mg every 2 weeks. Post hoc subgroup analyses were performed for patients with 0, ≥1, 1/2, or ≥3 prior surgeries, and for patients who had surgery within <3, 3 to <5, 5 to <10, or ≥10 years. Efficacy outcomes at 24 weeks included co-primary endpoints nasal polyp score (NPS) and nasal congestion (NC), and Lund-Mackay (LMK), 22-item Sino-Nasal Outcome Test (SNOT-22), and smell scores. RESULTS: Of 724 patients randomized, 459 (63.4%) had ≥1 prior surgery. Baseline sinus disease (NPS, NC, LMK) and olfactory dysfunction (University of Pennsylvania Smell Identification Test [UPSIT] and loss of smell) scores were worse for patients with ≥3 prior surgeries vs no surgery. Baseline NPS and LMK were worse in patients with <3 years since last surgery than in patients with ≥5 years since last surgery. Dupilumab significantly improved all outcome measures vs placebo in all subgroups by number of surgeries and by time since last surgery. Improvements in NPS and LMK were greater in patients with <3 years since last surgery than patients with ≥5 years. Safety results were consistent with the known dupilumab safety profile. CONCLUSION: Dupilumab improved CRSwNP outcomes irrespective of surgery history, with greater improvements in endoscopic outcomes in patients with shorter duration since last 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 | 0.001 | 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 | 0.000 |
| 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".