Changing Paradigms in the Treatment of Severe Asthma: The Role of Biologic Therapies
Bibliographic record
Abstract
Cytokine antagonists are monoclonal antibodies that offer new treatment options for refractory asthma but will also increase complexity because they are effective only for patients with certain asthma subtypes that remain to be more clearly defined. The clinical and inflammatory heterogeneity within refractory asthma makes it difficult to manage the disease and to determine which, if any, biologic therapy is suitable for a specific patient. The purpose of this article is to provide a data-driven discussion to clarify the use of biologic therapies in patients with refractory asthma. We first discuss the epidemiology and pathophysiology of refractory asthma. We then interpret current evidence for biomarkers of eosinophilic or type 2-high asthma so that clinicians can determine potential treatments for patients based on knowledge of their effectiveness in specific asthma phenotypes. We then assess clinical data on the efficacy, safety, and mechanisms of action of approved and pipeline biologic therapies. We conclude by discussing the potential of phenotyping or endotyping refractory asthma and how biologic therapies can play a role in treating patients with refractory asthma.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".