Comparative Effectiveness of Dupilumab Versus Sinus Surgery for Chronic Rhinosinusitis With Polyps: Systematic Review and a Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Current treatment paradigms recommend surgical intervention when conventional medical management proves ineffective in resolving chronic rhinosinusitis with nasal polyposis. OBJECTIVES: To assess and compare the efficacy of dupilumab and functional endoscopic sinus surgery (FESS) for the treatment of chronic rhinosinusitis with nasal polyp (CRSwNP) over time. METHODS: Studies comparing CRSwNP patients who received dupilumab with those who underwent FESS were included. Outcome measures included the nasal congestion score (NCS), Sino-nasal Outcome Test-22 (SNOT-22), University of Pennsylvania Smell Identification Test-40 (UPSIT-40), and nasal polyp score (NPS). The risk of bias was evaluated using the Newcastle-Ottawa Scale. RESULTS: A total of 4 studies with 724 participants were included. The dupilumab group had a superior NCS, but an inferior NPS, compared to the FESS group during the follow-up period. The SNOT-22 score of the dupilumab group was inferior to that of the FESS group until 6 months posttreatment, but the scores were similar at around 1 year. A similar trend was observed for the UPSIT-40 score, but the score of the dupilumab group was higher at around 1 year. CONCLUSION: Functional endoscopic sinus surgery was more effective than dupilumab for several months after treatment. However, at 1 year after treatment, the effects of the 2 treatments became similar, with greater olfactory improvement seen in the dupilumab group.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.031 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".