Proinflammatory Cytokines and Early Neurological Worsening in Ischemic Stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The mechanisms for clinical deterioration in patients with ischemic stroke are not completely understood. Several proinflammatory cytokines are released early after the onset of brain ischemia, but it is unknown whether inflammation predisposes to neurological deterioration. We assessed the implication of interleukin (IL)-6 and tumor necrosis factor (TNF)-alpha in early neurological worsening in ischemic stroke. METHODS: Two hundred thirty-one patients consecutively admitted with first-ever ischemic cerebral infarction within the first 24 hours from onset were included. Neurological worsening was defined when the Canadian Stroke Scale (CSS) score fell at least 1 point during the first 48 hours after admission. IL-6 and TNF-alpha were determined in plasma and cerebrospinal fluid (CSF; n=81) obtained on admission. RESULTS: Eighty-three patients (35.9%) deteriorated within the first 48 hours. IL-6 in plasma (>21.5 pg/mL; OR 37.7, CI 11.9 to 118.8) or in CSF (>6.3 pg/mL; OR 13.1, CI 2.2 to 77.3) were independent factors for early clinical worsening, with multiple logistic regression. The association was statistically significant in all ischemic stroke subtypes as well as in subjects with cortical or subcortical infarctions. IL-6 in plasma was highly correlated with body temperature, glucose, fibrinogen, and infarct volume. CSF and plasma concentrations of TNF-alpha were also higher in patients who deteriorated, but the differences observed did not remain significant on multivariate analysis. CONCLUSIONS: In addition to participating in the acute-phase response that follows focal cerebral ischemia, IL-6 levels on admission are associated with early clinical deterioration. The association between IL-6 and early neurological worsening prevails without regard to the initial size, topography, or mechanism of the ischemic infarction.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".