Predictive value of inflammatory markers for functional outcomes in patients with ischemic stroke
Bibliographic record
Abstract
Background: Inflammatory processes have been proposed in the pathophysiology of ischemic stroke. The present study was designed to evaluate the relationship between tumor necrosis factor-alpha (TNF-α), interleukin 6 (IL-6), IL 1 beta (IL-1β), and high sensitivity C-reactive protein (hsCRP) with the prognosis and functional outcome in patients with less severe ischemic stroke. Methods: We measured the level of IL-1β, IL-6, hsCRP, and TNF-α on days 1 and 5 after stroke onset by enzyme-linked immunosorbent assay (ELISA). The infarct volume was assessed using Alberta Stroke Program Early CT Score (ASPECTS) and posterior circulation ASPECTS (pcASPECTS) score in brain computed tomography (CT) scan and magnetic resonance imaging (MRI). The severity of stroke was assessed by applying the National Institutes of Health Stroke Scale (NIHSS) and Modified Rankin Scale (MRS) in 24 hours on day 5 and after 3 months from stroke onset. Good outcome was defined as the third month MRS ≤ 2. The association of inflammatory markers and the course of stroke symptoms over time was examined. Results: Forty-four first-ever stroke patients without concurrent inflammatory diseases with a mean age of 65 years were included. The mean NIHSS and MRS in admission time were 6.5 ± 3.5 and 3.07, respectively. The day 1 and the day 5 levels of IL-1β, IL-6, hsCRP, and TNF-α were not significantly different in good and poor outcome groups (all P-values > 0.05). In addition, they were not significantly associated with the ASPECTS, pcASPECTS, and changes of NIHSS and MRS over time. Conclusion: The levels of hsCRP, IL-1β, IL-6, and TNF-α are not reliable predictors of functional outcomes in patients with less severe acute ischemic stroke (AIS).
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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 | 0.004 |
| 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 | 0.000 | 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".