Impact of benralizumab on asthma control, asthma-related quality of life and lung function in patients with poorly controlled eosinophilic asthma: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Benralizumab is a monoclonal antibody to the alpha subunit of the IL-5 receptor used in the management of severe, eosinophilic asthma. Although Benralizumab has been shown to significantly reduce asthma exacerbation rates in several Randomized Controlled Trials (RCTs), its impact on subjective asthma control, asthma-related quality of life and lung function is less clear. The purpose of this meta-analysis is to analyze the combined effect of Benralizumab on Asthma Control Questionnaire (ACQ) scores, Asthma Quality of Life Questionnaire (AQLQ) scores and pre-bronchodilator (pre-BD) FEV1 values in severe asthmatics with eosinophilia.METHODS: A comprehensive search of selected databases was performed to include randomized, phase 3 placebo-controlled clinical trials that compared the impact of Benralizumab on ACQ6 scores, AQLQ scores and pre-BD FEV1 values in severe asthmatics with eosinophilia. Random effect models were produced to compare the combined effect of Benralizumab treatment in comparison to placebo.RESULTS: Overall, Benralizumab treatment in asthmatic patients with eosinophilia resulted in significantly improved ACQ6 scores, AQLQ scores and pre-BD FEV1 values in comparison to placebo (Mean Difference: −0.24,95%CI: −0.32, −0.16, p-value: <0.00001); (Mean Difference: 0.23, 95%CI: 0.14, 0.32, p-value: <0.00001); (Mean Difference: 0.11, 95% CI 0.08, 0.15, p-value: <0.00004), respectively.CONCLUSIONS: Our meta-analysis demonstrates that treatment with Benralizumab in patients with severe asthma associated with eosinophilia significantly improves asthma control, asthma-related quality of life and lung function. We believe these findings can provide evidenced-based recommendations for the use of Benralizumab in asthmatic patients with eosinophilia.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.035 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".