Achieving remission in severe asthma
Bibliographic record
Abstract
Severe asthma affects 5-10 % of asthma patients worldwide, imposing a significant burden due to an increased risk of mortality, impaired quality of life, and substantial economic costs. Recent advancements in biologic therapies have transformed asthma management by targeting specific inflammatory pathways, particularly type 2 inflammation. Biologic treatments such as omalizumab, mepolizumab, reslizumab, benralizumab, and dupilumab have demonstrated efficacy in reducing exacerbations, improving lung function, and achieving clinical remission in a subset of patients. This review provides an overview of the mechanisms of action, indications, and treatment efficacy of biologics used in asthma management. We also explore the concept of asthma remission and the potential for achieving it through biologic therapies and complementary strategies, including optimized inhaler use, macrolides, and bronchial thermoplasty. In addition, we discuss how to choose among these treatments wisely and examine the limitations of each biologic therapy. Despite these advancements, clinical remission rates remain modest, underscoring the need for refined patient selection. Emerging tools such as airway biomarkers, proteomics, and advanced imaging techniques offer promising avenues to improve diagnosis and personalize treatment approaches. Future research focused on making advanced biomarkers more accessible and feasible for point-of-care testing will enhance treatment precision. The next step will be integrating a multiomics approach into personalized asthma management for severe disease, further improving asthma control, achieving sustained remission, and ultimately reducing the burden of severe 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".