Baseline patient factors impact on the clinical efficacy of benralizumab for severe asthma
Bibliographic record
Abstract
Benralizumab is an anti-eosinophilic monoclonal antibody that reduces exacerbations and improves lung function for patients with severe, uncontrolled asthma with eosinophilic inflammation. We evaluated the impact of baseline factors on benralizumab efficacy for patients with severe asthma. This analysis used pooled data from the SIROCCO ( ClinicalTrials.gov identifier NCT01928771 ) and CALIMA ( ClinicalTrials.gov identifier NCT01914757 ) Phase III studies. Patients aged 12–75 years with severe, uncontrolled asthma receiving high-dosage inhaled corticosteroids plus long-acting β 2 -agonists received benralizumab 30 mg subcutaneously every 8 weeks (Q8W, first three doses every 4 weeks (Q4W)), Q4W or placebo. Baseline factors that influenced benralizumab efficacy were evaluated, including oral corticosteroid (OCS) use, nasal polyposis, pre-bronchodilator forced vital capacity (FVC), prior year exacerbations and age at diagnosis. Efficacy outcomes included annual exacerbation rate and change in pre-bronchodilator forced expiratory volume in 1 s at treatment end relative to placebo. Benralizumab Q8W treatment effect was enhanced with each baseline factor for all patients and those with ≥300 eosinophils·μL −1 relative to the overall population. OCS use, nasal polyposis and FVC <65% of predicted were associated with greater benralizumab Q8W responsiveness for reduced exacerbation rate for patients with <300 eosinophils·μL −1 . Baseline clinical factors and blood eosinophil counts can help identify patients potentially responsive to benralizumab.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".