Sustained improvement of asthma control over 6 months of benralizumab treatment in Canadians with severe asthma
Bibliographic record
Abstract
Background: In Canada nearly 8% of patients with asthma have severe disease. Benralizumab is an anti-IL-5Rα antibody for add-on maintenance therapy of severe eosinophilic asthma. Objective: To describe the change in asthma control in the first 6 months of benralizumab treatment. Methods: POWER is a prospective observational study (NCT03833141; POWER, part of XALOC). Patients were recruited from 23 clinics across Canada from 2019, with an inclusion criteria of blood eosinophils ≥300c/µL, Asthma Control Questionnaire (ACQ-6) score ≥1.5, and benralizumab naïveté. Treatment response to benralizumab was assessed via patient-reported ACQ-6 at baseline and 24 weeks following first treatment. Asthma control status was reported by full control (mean<0.75), partial control (0.75-1.5), and poor control (>1.5). Minimum clinically important difference (MCID) in ACQ-6 is a change of 0.5 units. Results: 88 patients were included in this interim analysis. At week 24, the change in ACQ-6 met the MCID regardless of control status. The mean change for the full cohort was -1.5 (95%CI: -1.8, -1.2), triple the MCID (Figure 1). Conclusion: Patients demonstrated a significant and clinically meaningful difference in asthma control 6 months after initiating treatment with benralizumab. These results show a sustained and impactful benefit in patients initiating therapy with benralizumab.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".