Treatable traits can be identified in a severe asthma registry and predict future exacerbations
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: A new taxonomic and management approach, termed treatable traits, has been proposed for airway diseases including severe asthma. This study examined whether treatable traits could be identified using registry data and whether particular treatable traits were associated with future exacerbation risk. METHODS: The Australasian Severe Asthma Web-Based Database (SAWD) enrolled 434 participants with severe asthma and a comparison group of 102 participants with non-severe asthma. Published treatable traits were mapped to registry data fields and their prevalence was described. Participants were characterized at baseline and every 6 months for 24 months. RESULTS: In SAWD, 24 treatable traits were identified in three domains: pulmonary, extrapulmonary and behavioural/risk factors. Patients with severe asthma expressed more pulmonary and extrapulmonary treatable traits than non-severe asthma. Allergic sensitization, upper-airway disease, airflow limitation, eosinophilic inflammation and frequent exacerbations were common in severe asthma. Ten traits predicted exacerbation risk; among the strongest were being prone to exacerbations, depression, inhaler device polypharmacy, vocal cord dysfunction and obstructive sleep apnoea. CONCLUSION: Treatable traits can be assessed using a severe asthma registry. In severe asthma, patients express more treatable traits than non-severe asthma. Traits may be associated with future asthma exacerbation risk demonstrating the clinical utility of assessing treatable traits.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".