Challenges in severe asthma: Do we need new drugs or new biomarkers?
Bibliographic record
Abstract
Severe asthma is a complex, heterogenous airway condition. There have been significant advances in severe asthma management in the past decade using monoclonal antibody therapies that target the inflammatory component of the disease. Patient selection has been paramount for the success of these biologicals, leading to significant interest in biomarkers to guide treatment. Some severe asthmatics remain suboptimally controlled despite trials of biologicals and many of these patients still require chronic systemic corticosteroids. New therapeutics are currently in development to address this unmet need. However, whether these patients could be better treated by using novel biomarkers that inform selection among currently available biologics, and that objectively measure disease control is unclear. In this review, we examine the currently used biomarkers that guide severe asthma management and emerging biomarkers that may improve asthma therapy in the future.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".