Asthma control and QoL among Canadian patients with severe eosinophilic asthma (SEA): 2-year real-world results
Bibliographic record
Abstract
Introduction Evaluation of benralizumab use in adults with SEA found early and sustained asthma control and improved QoL in the Canadian POWER study at 6 months (CJRCCSM 2024;8(3):99-107). Long-term benralizumab data on asthma control and QoL are lacking in these patients. Aims and Objectives To assess SEA control and QoL outcomes in patients with uncontrolled SEA treated with benralizumab up to 2 years in a Canadian real-world setting Methods POWER ( NCT03833141 ) is a real-world, prospective, single-arm observational study of patients with uncontrolled SEA treated with benralizumab. Patients were recruited from 20 Canadian clinics from 2019–2022. Inclusion criteria were blood eosinophils ≥300 cells/µL, Asthma Control Questionnaire (ACQ-6) score ≥1.5, and benralizumab naïve. Change in ACQ-6 was measured up to 2 years after initiating benralizumab (minimum clinically important difference: change of 0.5 points). QoL was assessed with the standardized Asthma QoL Questionnaire (AQLQ(S)+12). Results Of 141 patients assessed at baseline, 56 were followed to week 104. Mean (95% CI) change in ACQ-6 at week 104 from baseline was -1.64 (-1.99, -1.28 [baseline: 3.26; week 104: 1.47]); 83.9% of patients achieved clinically meaningful improvement at week 104 from baseline. At week 104, 28.6% and 28.6% of patients were well and partially controlled. AQLQ(S)+12 mean (SD) total score improved from 3.15 (1.02) at baseline (n=130) to 5.14 (1.15) at week 104 (n=50); 92.0% of patients had clinically meaningful QoL improvement (ΔAQLQ(S)+12≥0.5). Conclusions Patients with SEA experienced continued clinically meaningful improvement in asthma control and QoL up to 2 years after initiating 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".