Precision medicine applications for severe asthma
Why this work is in the frame
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Bibliographic record
Abstract
Asthma is a heterogeneous condition in which multiple pathological pathways manifest with similar symptoms. Severe asthma (SA) is challenging to manage and comprises a significant health and economic burden. Many studies have been conducted in an attempt to define different clinical phenotypes that translate into biological endotypes, with the goal of tailoring treatment based on precision medicine. This review summarizes the current evidence for the treatments of SA, and in particular, the biologic treatments that are currently available: omalizumab, mepolizumab, reslizumab, benralizumab and dupilumab. We found only limited high-quality direct evidence regarding treatment with anti-IgE (omalizumab) in SA patients. Data regarding anti-interleukin (IL)-5 (mepolizumab, reslizumab and benralizumab) showed beneficial effects in severe eosinophilic asthma (SEA) with different levels of blood eosinophils used in clinical trials. Dupilumab, anti-IL-4/IL-13, was shown to be effective in SEA and is the only agent currently FDA-approved for the indication of oral corticosteroid dependent asthma, regardless of the blood eosinophil level. This review also summarizes the existing knowledge regarding the characteristics of the patient who may respond to the different therapies. As of today, more studies are needed to better understand the diverse mechanisms that underlie SA phenotypes. We have not yet adequately reached the goal of precision medicine. Additional studies are necessary in order to find novel surrogate markers that can predict the response to a specific biologic therapy, especially in patients who are oral corticosteroid dependent. In addition, efforts must be invested into research looking for new treatment options for patients with non-type-2 inflammation SA. Statement of novelty: we review the current evidence regarding tailored treatment therapies in SA, with a particular focus on the knowledge regarding patient selection for specific biologic treatments.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it