Phenotype and biomarkers in patients who initiated biologics therapy stratified by oral corticosteroid use in the International Severe Asthma Registry
Bibliographic record
Abstract
Background: Oral corticosteroids (OCS) are overused in severe asthma patients, but the profile of patients who use OCS is not sufficiently described. Aims: To compare the phenotype and biomarkers of severe asthma patients before biologic initiation by pre-biologic OCS use. Methods: This cross-sectional study used the International Severe Asthma Registry, a multinational prospective registry of asthma patients aged ≥18 years old on GINA step 4/5 therapy. Those with biologic initiation and ≥1 year of prior data were included and grouped by OCS use (non-users, intermittent users, and long-term [LTOCS] users). Results: In total, 4,305 patients were included (41% LTOCS users, 54% intermittent users, 5% non-users). Most patients had an eosinophilic phenotype and ≥1 type 2 comorbidity (Table). LTOCS users had the highest proportions of persistent airway obstruction and blood eosinophil count <150 cells/µL (Table). Long-term and intermittent OCS users had higher frequencies of uncontrolled asthma than non-users (Table). Conclusions: Most severe asthma patients had LTOCS/intermittent OCS use before initiating biologics. LTOCS users had the wort lung function, with distinct biomarker patterns. Reasons for poor asthma control in OCS users and OCS’ confounding effects on biomarkers need exploration. erj;64/suppl_68/PA438/F1 F1 F1
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".