Levels of Anti-Inflammatory Cytokines and Neurological Worsening in Acute Ischemic Stroke
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Mechanisms involved in stroke progression are incompletely understood. Ischemic brain injury is characterized by acute local inflammatory response mediated by cytokines. Anti-inflammatory cytokines act in a feedback loop to inhibit continued proinflammatory cytokine production. We assessed the implication of interleukin (IL)-10 and IL-4 in deteriorating ischemic stroke. METHODS: Two hundred thirty-one patients with ischemic stroke within the first 24 hours from onset were included. Neurological worsening was defined when the Canadian Stroke Scale score fell at least 1 point during the first 48 hours after admission. Anti-inflammatory cytokines were determined in plasma obtained on admission. RESULTS: Eighty-three patients (35.9%) worsened within the first 48 hours after stroke onset. Significantly lower concentrations of IL-10 were found in patients with neurological worsening (P<0.05), but IL-4 levels were similar in patients with or without deterioration. Lower plasma concentrations of IL-10 (<6 pg/mL) were associated with clinical worsening on multivariate analysis (odds ratio=3.1, 95% CI=1.1 to 8.9) independently of hyperthermia, hyperglycemia, or neurological condition on admission. Further analysis disclosed that early worsening was independently associated with lower IL-10 plasma levels in patients with subcortical infarcts or lacunar stroke but not in patients with cortical lesions. CONCLUSIONS: Anti-inflammatory cytokine IL-10 is associated with the early clinical course of patients with acute ischemic stroke, especially in patients with small vessel disease or subcortical infarctions.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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 it