Levels of Anti-Inflammatory Cytokines and Neurological Worsening in Acute Ischemic Stroke
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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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".