Benralizumab reduces sputum ANCA in patients with eosinophilic granulomatosis with polyangiitis
Bibliographic record
Abstract
Extract Eosinophilic Granulomatosis with Polyangiitis (EGPA) is a rare vasculitis characterized by eosinophilic inflammation and autoimmune features, with anti-neutrophil cytoplasmic antibodies (ANCA) against myeloperoxidase (MPO) (pANCA) detectable in serum in up to 40% of patients. Seronegative EGPA can be associated with severe cardiac and pulmonary complications, including uncontrolled asthma [1]. We previously observed ANCA in sputum (spANCA) in 74% of EGPA patients, regardless of serum pANCA status, all of whom had severe asthma requiring high-dose oral corticosteroids [2]. These findings suggest that a localized airway autoimmune response may drive disease severity. Mepolizumab and benralizumab—monoclonal antibodies targeting IL-5 and IL-5Rα, respectively—are approved therapies that induce remission and reduce corticosteroid use in EGPA. Benralizumab has demonstrated non-inferiority to mepolizumab in relapsing or refractory cases [3]. Our primary objective was to investigate whether benralizumab and mepolizumab exert differential effects on airway autoimmune responses in EGPA. Airway autoimmunity is increasingly recognized as a key determinant of pulmonary outcomes in EGPA, and its modulation may influence treatment efficacy [2, 4]. To address this, we analyzed sputum, nasal lavage, and blood samples from sixteen patients enrolled in the randomized, double-blind MANDARA trial ( NCT04157348 ).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".