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 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".