High eosinophil counts predict decline in FEV<sub>1</sub>: results from the CanCOLD study
Bibliographic record
Abstract
Introduction The aim of this study was to examine the association between blood eosinophil levels and the decline in lung function in individuals aged >40 years from the general population. Methods The study evaluated the eosinophil counts from thawed blood in 1120 participants (mean age 65 years) from the prospective population-based Canadian Cohort of Obstructive Lung Disease (CanCOLD) study. Participants answered interviewer-administered respiratory questionnaires and performed pre-/post-bronchodilator spirometric tests at 18-month intervals; computed tomography (CT) imaging was performed at baseline. Statistical analyses to describe the relationship between eosinophil levels and decline in forced expiratory volume in 1 s (FEV 1 ) were performed using random mixed-effects regression models with adjustments for demographics, smoking, baseline FEV 1 , ever-asthma and history of exacerbations in the previous 12 months. CT measurements were compared between eosinophil subgroups using ANOVA. Results Participants who had a peripheral eosinophil count of ≥300 cells·µL −1 (n=273) had a greater decline in FEV 1 compared with those with eosinophil counts of <150 cells·µL −1 (n=430; p=0.003) (reference group) and 150–<300 cells·µL −1 (n=417; p=0.003). The absolute change in FEV 1 was −32.99 mL·year −1 for participants with eosinophil counts <150 cells·µL −1 ; −38.78 mL·year −1 for those with 150–<300 cells·µL −1 and −67.30 mL·year −1 for participants with ≥300 cells·µL −1 . In COPD, higher eosinophil count was associated with quantitative CT measurements reflecting both small and large airway abnormalities. Conclusion A blood eosinophil count of ≥300 cells·µL −1 is an independent risk factor for accelerated lung function decline in older adults and is related to undetected structural airway abnormalities.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".