Clinical update on the use of biomarkers of airway inflammation in the management of asthma
Bibliographic record
Abstract
Biological markers are already used in the diagnosis and treatment of cardiovascular disease and cancer. Biomarkers have great potential use in the clinic as a noninvasive means to make more accurate diagnoses, monitor disease progression, and create personalized treatment regimes. Asthma is a heterogeneous disease with several different phenotypes, generally triggered by multiple gene-environment interactions. Pulmonary function tests are most often used objectively to confirm the diagnosis. However, airflow obstruction can be variable and thus missed using spirometry. Furthermore, lung function measurements may not reflect the precise underlying pathological processes responsible for different phenotypes. Inhaled corticosteroids and β(2)-agonists have been the mainstay of asthma therapy for over 30 years, but the heterogeneity of the disease means not all asthmatics respond to the same treatment. High costs and undesired side effects of drugs also drive the need for better targeted treatment of asthma. Biomarkers have the potential to indicate an individual's disease phenotype and thereby guide clinicians in their decisions regarding treatment. This review focuses on biomarkers of airway inflammation which may help us to identify, monitor, and guide treatment of asthmatics. We discuss biomarkers obtained from multiple physiological sources, including sputum, exhaled gases, exhaled breath condensate, serum, and urine. We discuss the inherent limitations and benefits of using biomarkers in a heterogeneous disease such as asthma. We also discuss how we may modify our study designs to improve the identification and potential use of potential biomarkers in asthma.
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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 | 0.000 |
| 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) | 0.000 | 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".