Efficacy and safety of treatment with biologicals for severe chronic rhinosinusitis with nasal polyps: A systematic review for the EAACI guidelines
Bibliographic record
Abstract
This systematic review evaluates the efficacy and safety of biologicals for chronic rhinosinusitis with nasal polyps (CRSwNP) compared with the standard of care. PubMed, Embase, and Cochrane Library were searched for RCTs. Critical and important CRSwNP-related outcomes were considered. The risk of bias and the certainty of the evidence were assessed using GRADE. RCTs evaluated (dupilumab-2, omalizumab-4, mepolizumab-2, and reslizumab-1) included 1236 adults, with follow-up of 20-64 weeks. Dupilumab reduces the need for surgery (NFS) or oral corticosteroid (OCS) use (RR 0.28; 95% CI 0.20-0.39, moderate certainty) and improves with high certainty smell evaluated with UPSIT score (mean difference (MD) +10.54; 95% CI +9.24 to +11.84) and quality of life (QoL) evaluated with SNOT-22 (MD -19.14; 95% CI -22.80 to -15.47), with fewer treatment-related adverse events (TAEs) (RR 0.95; 95% CI 0.89-1.02, moderate certainty). Omalizumab reduces NFS (RR 0.85; 95% CI 0.78-0.92, high certainty), decreases OCS use (RR 0.38; 95% CI 0.10-1.38, moderate certainty), and improves high certainty smell (MD +3.84; 95% CI +3.64 to +4.04) and QoL (MD -15.65; 95% CI -16.16 to -15.13), with increased TAE (RR 1.73; 95% CI 0.60-5.03, moderate certainty). There is low certainty for mepolizumab reducing NFS (RR 0.78; 95% CI 0.64-0.94) and improving QoL (MD -13.3; 95% CI -23.93 to -2.67) and smell (MD +0.7; 95% CI -0.48 to +1.88), with increased TAEs (RR 1.64; 95% CI 0.41-6.50). The evidence for reslizumab is very uncertain.
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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 | 0.011 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.012 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".