Applying Precision Medicine to the Heterogeneity of Asthma Attacks
Bibliographic record
Abstract
TOPIC IMPORTANCE: The standard of care for management of asthma attacks has remained unchanged for 70 years, relying on a symptom-based, severity-stratified approach. Severe asthma attacks are defined by a worsening of asthma requiring oral corticosteroid (OCS) treatment for unresolved symptoms for at least 48 hours, decreased lung function, or both. The 1-size-fits-all strategy with OCS treatment overlooks the biological mechanisms driving attacks and may lead to suboptimal outcomes. Importantly, OCS-related toxicities lead to significant morbidity, and cumulative OCS use has been associated with increased mortality. Antibiotics, often used indiscriminately, also increase adverse events and antimicrobial resistance. REVIEW FINDINGS: Recent studies have highlighted the heterogeneity of asthma attacks across clinical, etiologic, and therapeutic dimensions. Biomarker-informed assessments using blood eosinophils, exhaled nitric oxide (Feno), and point-of-care microbial molecular testing have improved the evaluation of attacks. Observational studies and trials have explored biomarker-guided management to reduce OCS and antibiotic use, potentially improving outcomes. Distinct inflammatory and OCS response profiles were identified in patients receiving biologics, emphasizing the complexity of attacks and the importance of residual (untreated) type 2 inflammatory pathways. Studies of the airway microbiome revealed that microbial dysbiosis is associated with clinical and inflammatory clusters. SUMMARY: Asthma attacks are complex episodes with diverse causes, endotypes, and phenotypes. Emerging evidence supports incorporating biomarkers (blood eosinophils, Feno, and microbial testing) into clinical assessment to refine management. Recent evidence expands our understanding of exacerbation mechanisms, highlighting the need for tailored management strategies. Recognizing asthma heterogeneity could shift care toward precision medicine, reducing OCS reliance and improving patient outcomes.
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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 | 0.012 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".