Omalizumab in allergic eosinophilic asthma and lung eosinophil numbers: A 78-week randomized controlled trial
Bibliographic record
Abstract
A biopsy study has previously shown significant anti-inflammatory activity of omalizumab (OMA) in mild asthma, but how these results apply to more severe disease is uncertain. We studied the impact of OMA on airway inflammation and remodelling in persistent moderate-to-severe allergic eosinophilic asthma. Patients with allergic asthma and high sputum eosinophils, treated with ICS/LABA, were randomized 2:1 to OMA or placebo for 78 weeks. Bronchial biopsy was performed at baseline (BL) and end of treatment (EOT). Primary endpoint was change from BL (Δ) in total sub-epithelial eosinophils at EOT. Other outcomes included Δ in reticular basement membrane thickness (RBMT) and sub-epithelial IgE levels. Efficacy was assessed by physician’s Global Evaluation of Treatment Effectiveness (GETE) and clinically significant exacerbations (CSE). 36 patients (mean age 43.1 years) were recruited (OMA, n=23; placebo, n=13). Response by GETE was 56.5% for OMA and 25.0% for placebo. Percentage of patients with ≥1 CSE was 26.1% for OMA and 41.7% for placebo. However, there were no significant between-group differences in primary outcome or RBMT. Sub-epithelial IgE suppression was demonstrated at EOT, but was not associated with changes in sub-epithelial eosinophil numbers. Consistent with past studies, OMA was clinically effective in moderate-to-severe allergic eosinophilic asthma, but a relationship between lung eosinophils and response was not apparent. Although inconclusive, this study questions whether the efficacy of OMA is independent of eosinophil suppression and suggests that further investigations should explore other anti-inflammatory mechanisms.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".