Blood eosinophil count as predictor of asthma exacerbation. A meta‐analysis
Bibliographic record
Abstract
BACKGROUND: Evidence about the association of high blood eosinophil count with asthma exacerbation is inconsistent and unclear. The objective of this meta-analysis was to determine whether elevated blood eosinophil count predicts asthma exacerbation. METHODS: We searched MEDLINE, EMBASE, and additional databases, without any language restriction. We also checked the reference lists of the included studies and of relevant systematic reviews. The main outcome was the occurrence of asthma exacerbation. We calculated global pooled odds ratios (ORs) and their 95% confidence intervals (CIs) and performed predefined subgroup analyses. We appraised the quality of the studies using Newcastle-Ottawa Scale, examined the heterogeneity between studies, assessed publication bias, and carried out sensitivity analyses. RESULTS: Among 1567 retrieved publications, 23 observational studies comprising 155,772 participants met the inclusion criteria. High blood eosinophil count was associated with higher odds of asthma exacerbation [OR: 1.31 (95% CI: 1.16, 1.49)], specifically with asthma-related outpatient visits [OR: 1.46 (95% CI: 1.25, 1.70)] and emergency department visits [OR: 1.63 (95% CI: 1.29, 2.07)]. A significant association was observed starting from an eosinophils' cutoff value of 200 cells/μl. The association was observed for cohort studies [OR: 1.30 (95%CI: 1.13, 1.49)], North American studies [OR: 1.43 (95%CI: 1.31, 1.57)], Asian populations [OR: 1.67 (95%CI: 1.34, 2.08)], children [OR: 1.38 (95%CI: 1.22, 1.56)], and studies that adjusted for inhaled corticosteroids therapy [OR: 1.42 (95%CI: 1.28, 1.56)]. CONCLUSIONS: Blood eosinophil counts ≥ 200 cells/µL are associated with asthma exacerbation. Blood eosinophil count is a modifiable factor that could be addressed in asthma management strategies.
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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 | 0.016 | 0.025 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.068 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".