Can eosinophilia and neutrophil–lymphocyte ratio predict hospitalization in asthma exacerbation?
Bibliographic record
Abstract
OBJECTIVE: Asthma is one of the most common diseases amongst children. Blood eosinophil count and neutrophil-lymphocyte ratio (NLR) are known as markers for phenotyping asthma. This study was performed to investigate blood eosinophil count and NLR as predictors of hospitalization in pediatric asthma exacerbations. DATA SOURCES AND STUDY SELECTIONS: In this cross-sectional study, children admitted to hospital ward for more severe asthma exacerbation were compared with non-hospitalized children with moderate to severe asthma exacerbation whose asthma exacerbation was managed in emergency department or outpatient clinic. We investigated patients' characteristic and factors associated with hospitalization. RESULTS: A total of 211 children with moderate to severe asthma exacerbation (mean age [Formula: see text] years old) were enrolled in the study including 91 hospitalized patients and 120 non-hospitalized patients. For the prediction of hospitalization, an ROC Curve analysis was performed and revealed a cut-off of 298 cells/µL and 2.52 of blood eosinophil count and NLR, respectively. In multivariate analysis, not using an asthma action plan (OR 2.22, 95% CI 1.09-4.49; P = 0.027), a blood eosinophil count [Formula: see text] 298 (OR 8.79, 95% CI 4.44-17.4; P < 0.001) and an NLR [Formula: see text] 2.52 (OR 2.13, 95% CI 1.09-4.14; P = 0.027) were associated with hospitalization. CONCLUSION: Blood eosinophil count and NLR were found to be higher in hospitalized children with more severe asthma exacerbation compared to non-hospitalized patients. These markers can be indicators for asthma exacerbation severity.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".