Exacerbation risk in severe asthma is stratified by inflammatory phenotype using longitudinal measures of sputum eosinophils
Bibliographic record
Abstract
BACKGROUND: Airway inflammatory phenotyping is increasingly applied to subjects with asthma. However, its relationship to clinical outcomes in difficult asthma is incompletely elucidated. OBJECTIVE: The goal of our study was to determine the relationship between exacerbation rates and phenotypes of difficult asthma based on the longitudinal measures of sputum eosinophils and neutrophils. METHODS: Subjects in the longitudinal observational study from two tertiary care centres that completed 1 year of observation and provided at least three sputum samples were classified by inflammatory phenotypes using previously established thresholds. Kaplan-Meier curves and univariable and multivariable Cox proportional hazard models were used to determine the association between inflammatory phenotypes and exacerbation rate. RESULTS: During the study, 115 exacerbations occurred in 73 severe asthmatic subjects. Subjects with the persistently eosinophilic phenotype had a significantly shorter time to first exacerbation and greater risk of exacerbation over a 1-year period than those with the non-eosinophilic phenotype based on the univariable and multivariable Cox proportional hazard model (hazard ratio [HR], 3.24; 95% confidence interval [CI], 1.35-7.72; adjusted HR, 3.90; 95% CI, 1.34-11.36). No significant differences in time to first exacerbation or exacerbation risk over a 1-year period were observed among the neutrophilic phenotypes. CONCLUSIONS: The persistent eosinophilic phenotype is associated with increased exacerbation risk compared with the non-eosinophilic phenotype in severe asthma. No differences in time to first exacerbation or exacerbation risk over a 1-year period were detected among neutrophilic phenotypes.
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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 | 0.000 | 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) | 0.001 | 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 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".