Effect of COPD exacerbations on early lung function decline under maintenance therapy: blood eosinophil count asbiomarker
Bibliographic record
Abstract
Introduction: It is unknown whether exacerbations affect COPD progression under maintenance therapy. Objective: To study whether the association between COPD exacerbations and FEV1 decline depends on the therapy level and the blood eosinophil count (BEC) in a real-life setting. Methods: Patients diagnosed early with COPD (FEV1 % predicted 50-90), ≥35 years and a smoking history were followed for ≥3 years using data from the UK Optimum Patient Care Research Database and Clinical Practice Research Datalink. Multilevel linear regression models were used to analyse effects of the mean annual exacerbation rate after starting the highest maintenance therapy on FEV1 decline stratified by therapy level. Effect modification by BEC (± 2 years) was studied through interaction terms. Results: Of 12,178 patients included, 8,981 (74%) received inhaled corticosteroids (ICS). Overall, each exacerbation/year increase was associated with 5.6 ml/year extra decline (95%CI: 4.9;6.4). The largest effect was found in patients not receiving ICS with BEC ≥0.35x109/L (17%): 19.4 ml/year (12.0;26.7), significantly stronger than in patients with BEC 0.05-0.34x109/L (Figure). No effect of exacerbations under ICS therapy was found in those with BEC ≥0.45x109/L (11%): 1.0 ml/year (-2.5;4.5). Conclusion: Frequent exacerbators with high blood eosinophil counts show rapid COPD progression when not treated with ICS.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".