Efficacy of mepolizumab treatment in patients with chronicrhinosinusitis with nasal polyps: a single-centre, real-life study
Bibliographic record
Abstract
Introduction: The anti-interleukin (IL)-5 antibody mepolizumab has been used in the treatment of patients with uncontrolled severe eosinophilic asthma.Aim: In this real-life study, we aimed to evaluate the effect of mepolizumab on nasal symptoms in patients with chronic rhinosinusitis with nasal polyps (CRSwNP).Material and methods: Adult patients (> 18 years old) with concomitant CRSwNP, who were treated with mepolizumab at a dose of 100 mg every 4 weeks for at least one year for severe eosinophilic asthma, between 2021 and 2022, were evaluated retrospectively.Asthma control testing (ACT), Sino-Nasal Outcome Test (SNOT-22), and a visual analogue scale (VAS) of 1 to 10 (from 1 to 10) for nasal symptoms were compared at baseline (pre-mepolizumab treatment), and at 6 and 12 months post-treatment.The need for endoscopic sinus surgery (ESS) and oral corticosteroid (OCS) was compared at 12 months pre-mepolizumab and post-mepolizumab treatment.Results: The mean age of the 18 patients (9 (50%) males and 9 (50%) females) was 42.33 ±15.9 years.Mepolizumab significantly reduced the number of endoscopic sinus surgeries (ESSs) (p = 0.002).The need for shortcourse oral corticosteroid (OCS) decreased significantly after mepolizumab treatment (p = 0.029).Statistically significant improvements were found in the ACT, SNOT-22, and nasal symptom scores at 6 and 12 months post-treatment when compared to baseline.No side effects were observed post-treatment.Conclusions: Mepolizumab improved nasal symptoms and reduced the need for OCS and ESS in patients with CRSwNP.However, the results obtained in the study should be confirmed with real-life studies involving larger numbers of patients.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 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".