Blood eosinophil (EOS) count, exacerbation rate and response to roflumilast in patients with severe COPD
Bibliographic record
Abstract
Introduction: Studies suggest that high blood EOS count may be a marker of increased exacerbation risk in COPD patients (pts) with a history of exacerbations. We investigated the effect of blood EOS count on the efficacy of roflumilast (ROF) on COPD exacerbation rate. Methods: A post-hoc analysis was performed on two trials (REACT [NCT01329029], RE2SPOND [NCT01443845]) in which pts with severe COPD and ≥2 exacerbations in the previous year were randomised to ROF 500μg od or placebo (PBO) added to ICS/LABA±LAMA for 52 weeks. Primary endpoint was rate of moderate or severe COPD exacerbations/pt/year. Exacerbation rates were analysed by baseline EOS cell count strata (<150 and ≥150 cells/mm3). Results: Of 4287 pts, 2146 had an EOS count ≥150 cells/mm3 at entry. ROF reduced moderate or severe exacerbations rates by 19.1% (p=0.002) in pts with EOS ≥150 cells/mm3 and by 3.8% (p=0.605) in pts with EOS <150 cells/mm3 vs PBO (Table). Baseline demographics were balanced across EOS strata, including history of COPD exacerbations and hospitalisation for COPD exacerbation in the year prior. Conclusions: In this post-hoc analysis, PBO-treated COPD pts with higher blood baseline EOS counts had more exacerbations. Pts with higher blood baseline EOS counts derived greater benefit from ROF in exacerbation risk reduction compared to PBO.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".