Characteristics of long-term oral corticosteroid users stratified by blood eosinophil count in the International Severe Asthma Registry
Bibliographic record
Abstract
<bold>Background:</bold> Long-term oral corticosteroid (LTOCS) use is common in severe asthma (SA) Subsequent management of patients on LTOCS can be informed by endotypic features, e.g. blood eosinophil count (BEC). <bold>Aims:</bold> To characterize the phenotype and endotype of patients with SA and LTOCS use before biologics initiation, stratified by BEC. <bold>Methods:</bold> This cross-sectional study used the International Severe Asthma Registry, a multinational prospective registry of patients aged ≥18 years old with asthma on GINA step 4/5 therapy. Patients with LTOCS use and biologics initiation with ≥1 year of prior data were included, grouped by BEC (<150 vs ≥150 cells/µL). <bold>Results:</bold> In total, 1,288 patients were included (369 with BEC <150 cells/µL, 919 with BEC ≥150 cells/µL). Most had ≥1 type 2 comorbidity with elevated IgE (≥75 IU/mL) and/or FeNO (≥20 ppb; Table). More of the high-BEC group had uncontrolled/partially controlled asthma and/or persistent airway obstruction, but slightly fewer had daily OCS dose >5mg (Table). In the year before biologic initiation, the high-BEC group had more frequent asthma-related emergency department visits, but not exacerbations or hospitalizations (Table). <bold>Conclusion:</bold> Patients with SA and LTOCS use had a high steroid burden, among whom, those with high-BEC had worse asthma control and lung function. Better access to steroid-sparing therapies is needed regardless of BEC. <fig><object-id>erj;64/suppl_68/PA439/F1</object-id><object-id>F1</object-id><object-id>F1</object-id><graphic></graphic></fig>
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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) | 0.000 | 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".