Early Neutrophilia Is Associated With Volume of Ischemic Tissue in Acute Stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Few data exist on the relationship between differential subpopulations of peripheral leukocytes and early cerebral infarct size in ischemic stroke. Using diffusion-weighted MR imaging (DWI), we assessed the relationship of early total and differential peripheral leukocyte counts and volume of ischemic tissue in acute stroke. METHODS: All included patents had laboratory investigations and neuroimaging collected within 24 hours of stroke onset. Total peripheral leukocyte counts and differential counts were analyzed individually and by quartiles. DWI lesions were outlined using a semiautomated threshold technique. The relationship between leukocyte quartiles and DWI infarct volumes was examined using multivariate quartile regression. RESULTS: 173 patients met study inclusion criteria. Median age was 73 years. Total leukocyte counts and DWI volumes showed a strong correlation (Spearman rho=0.371, P<000.1). Median DWI volumes (mL) for successive neutrophil quartiles were: 1.3, 1.3, 3.2, and 20.4 (P for trend <0.001). Median DWI volumes (mL) for successive lymphocyte quartiles were: 3.2, 8.1, 1.3, and 1.5 (P=0.004). After multivariate analysis, larger DWI volume remained strongly associated with higher total leukocyte and neutrophil counts (both probability values <0.001), but not with lymphocyte count (P=0.4971). Compared with the lowest quartiles, DWI volumes were 8.7 mL and 12.9 mL larger in the highest quartiles of leukocyte and neutrophil counts, respectively. CONCLUSIONS: Higher peripheral leukocyte and neutrophil counts, but not lymphocyte counts, are associated with larger infarct volumes in acute ischemic stroke. Attenuating neutrophilic response early after ischemic stroke may be a viable therapeutic strategy and warrants further study.
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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 | 0.000 | 0.003 |
| 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 | 0.000 |
| 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".