Asthma biomarkers in the age of biologics
Bibliographic record
Abstract
The heterogeneous nature of asthma has been understood for decades, but the precise categorization of asthma has taken on new clinical importance in the era of specific biologic therapy. The simple categories of allergic and non-allergic asthma have given way to more precise phenotypes that hint at underlying biologic mechanisms of variable airflow limitation and airways inflammation. Understanding these mechanisms is of particular importance for the approximately 10% of patients with severe asthma. Biomarkers that aid in phenotyping allow physicians to "personalize" treatment with targeted biologic agents. Unfortunately, testing for these biomarkers is not routine in patients whose asthma is refractory to standard therapy. Scientific advances in the recognition of sensitive and specific biomarkers are steadily outpacing the clinical availability of reliable and non-invasive assessment methods designed for the prompt and specific diagnosis, classification, treatment, and monitoring of severe asthma patients. This article provides a practical overview of current biomarkers and testing methods for prompt, effective management of patients with severe asthma that is refractory to standard therapy.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".