The Association Between Inflammatory Biomarkers and Vitamin D Level With the Evolution and Severity of Stroke
Bibliographic record
Abstract
Introduction: Vitamin D deficiency has been linked to the evolution of ischemic stroke, but the data regarding the association between stroke severity and vitamin D level is scarce. Methods: Patients with first-ever ischemic stroke in the middle cerebral artery territory, within seven days after the stroke, were recruited. The control group included age- and gender-matched individuals. We compared 25-OH vitamin D (vitamin D), high sensitive C-reactive protein (hsCRP), serum amyloid A (SAA), and osteopontin levels between stroke patients and the control group. The association between stroke severity according to the National Institutes of Health Stroke Scale (NIHSS) and the Alberta stroke program early CT score (ASPECTS) and levels of vitamin D and inflammatory biomarkers were also studied. Results: There was an association between hypertension (P=0.035), diabetes mellitus (P=0.043), smoking (P=0.016), history of ischemic heart disease (P=0.002), higher SAA (P<0.001), higher hsCRP (P<0.001), and lower vitamin D levels (P=0.002) and stroke evolution in a case-control study. Meanwhile, in stroke patients, its severity was associated with higher SAA (P=0.04) and hsCRP (P=0.001), and lower vitamin D levels (P=0.043) according to clinical scale (higher admission NIHSS). According to the ASPECT score, higher SAA (P=0.017) and hsCRP (P=0.007), but not lower vitamin D levels, were associated with more infarct areas (P=0.149). Conclusion: Vitamin D may play a role in both the evolution and severity of stroke.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".