White blood cell count is an independent predictor of outcomes after acute ischaemic stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: In patients with ischaemic stroke, elevated white blood cell count (WBC) has been associated with stroke severity on admission and poor functional outcome. However, previous studies did not control for confounding factors. We hypothesized that higher WBC is an independent predictor of stroke severity, greater degree of disability and 30-day mortality after acute ischaemic stroke. METHODS: Data from the Registry of the Canadian Stroke Network on consecutive patients with acute ischaemic stroke admitted between July 2003 and March 2008 were used. Patients were divided into groups as follows: low WBC (0.1-4 × 10(-9) /l), normal WBC (4.1-10 × 10(-9) /l) and high WBC (10.1-40 × 10(-9) /l). Primary outcome measures were the frequency of moderate/severe strokes on admission (Canadian Neurological Scale ≤ 8), greater degree of disability at discharge (modified Rankin score 3-6) and 30-day mortality. Regression analyses were performed adjusting for confounders. RESULTS: In total, 8829 patients were included. After adjustment for major potential confounders, every 1 × 10(-9) /l increase in WBC was associated with stroke severity on admission [odds ratio (OR) 1.09; 95%CI 1.07-1.10; P < 0.0001), disability at discharge (OR 1.04; 95%CI 1.02-1.06; P = 0.0005) and 30-day mortality (hazard ratio 1.07; 95%CI 1.05-1.08; P < 0.0001). The Kaplan-Meier curves indicate that elevated WBC is associated with higher mortality after acute ischaemic stroke (P = 0.001). CONCLUSIONS: In patients with acute ischaemic stroke, higher WBC on admission is an independent predictor of stroke severity on admission, greater degree of disability at discharge and 30-day mortality. These results reinforce the need for further studies focused on immunomodulation therapy targeting inflammatory response following acute ischaemic stroke.
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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 | 0.007 |
| 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 | 0.001 | 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".