Eligibility of monoclonal antibody-based therapy for patients with severe asthma: a Canadian cross-sectional perspective
Bibliographic record
Abstract
BACKGROUND: Based on immunologic phenotypes underlying asthma, use of monoclonal antibody based therapies is becoming the new standard of care for severe, corticosteroid refractory clinical symptoms. Patients may qualify for one or more of these targeted treatments, based on clinical characteristics and approved indications. However, the statistics are not well characterized, particularly in the Canadian population. METHODS: The objective of this observational study was to identify and describe the proportion of patients with severe asthma who were eligible for targeting IgE, IL-5, or both pathways of immunomodulation. We reviewed a cross-sectional cohort of patients in a Canadian Allergy and Immunology referral practice. We also compared demographic and clinical characteristics of each group. RESULTS: Of the 128 patients with severe asthma, 84 (66%) were eligible for omalizumab, 100 (78%) for mepolizumab, 52 (41%) for reslizumab, and 68 (53%) for benralizumab. Overlap in treatment eligibility varied; 68 (53%) patients were eligible for both omalizumab and mepolizumab, 47 (37%) were eligible for omalizumab and benralizumab, and 37 (29%) were eligible for all four medications. Patient demographics and clinical characteristics were similar, and levels of serum biomarkers varied based on locally approved prescribing criteria. CONCLUSION: In this severe asthma population from a Canadian Allergist's practice, one-third of individuals qualified for all currently available biologics. 41-78% were eligible for at least one mAb. Patients were most likely to be eligible for mepolizumab. Objective assessments to determine asthma phenotype, along with further characterization of safety profiles will lead to further advances in asthma management.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".