Baseline predictors of being exacerbation-free during 2 years of benralizumab treatment
Bibliographic record
Abstract
Background: Integrated analyses of Phase III trials are important in characterizing patients (pts) who have the greatest long-term benefits with benralizumab. Aims and Objectives: To examine relationship of exacerbation (EX) frequency during 2 years of benralizumab treatment with baseline (BL) characteristics and efficacy. Methods: We performed post-hoc subgroup integrated analysis of 48-week SIROCCO (Bleecker, Lancet 2016) and 56-week CALIMA (FitzGerald, Lancet 2016) with 56-week BORA extension study (Busse, Lancet Respir Med 2019). Pts receiving benralizumab 30 mg SC every 8 weeks who had BL blood eosinophil counts ≥300 cells/μL were assessed. Pts were evaluated based on EXs experienced during the 2-year analysis period (0, 1, ≥2). Results: BL pre-bronchodilator [BD] FEV1 (%), pre-BD FEV1/FVC, and prior requirement for mechanical ventilation were significantly different for pts with 0–≥2 EXs, with additional significant differences for pts with 0 vs. 2 EXs (table). Greater improvements in ACQ-6 and AQLQ(S)+12 scores were observed for pts with 0 vs. 1/≥2 EXs, while lesser improvement in FEV1 and ACQ-6 and AQLQ(S)+12 scores was observed for the ≥2 EX group vs. the other groups by 16 weeks and was sustained for 2 years. Conclusions: Pts who remained EX-free during 2 years of benralizumab treatment were older at asthma onset, had better lung function, smaller ICU asthma admission rate and BMI, and less gastroesophageal reflux.
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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 | 0.003 |
| 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 | 0.001 | 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".