Soluble ST2 Predicts Poor Functional Outcome in Acute Ischemic Stroke Patients
Bibliographic record
Abstract
INTRODUCTION: There are very limited data on the role of biomarkers correlating with the outcome in acute ischemic stroke (AIS). We evaluated the predictive values of the plasma concentrations of soluble serum stimulation-2 (sST2), matrix metalloproteinase-9 (MMP-9), and claudin-5 in AIS. METHODS: The biomarker levels in the plasma samples of consecutive AIS patients collected at baseline, 12 h, and 24 h from stroke onset were quantified using immunoassays. Stroke severity was assessed using the National Institutes of Health Stroke Scale (NIHSS) and functional outcome at 90 days using the modified Rankin Scale (mRS), with scores above 3 defined as poor outcome. Receiver operating characteristic curve analysis and multiple logistic regression were performed for evaluating the discriminative power of each marker. RESULTS: We included 108 patients in the study (mean age 62.3 ± 11.7 years). Median NIHSS score was 12 (interquartile range 8-18). High baseline glucose levels, systolic blood pressure, baseline NIHSS, low Alberta Stroke Program Early CT Score, and hemorrhagic transformation were associated with poor outcomes. Elevated sST2 at 12 h (50.4 ± 51.0 ng/mL; p = 0.047) and 24 h (81.8 ± 101.3 ng/mL; p = 0.001) positively correlated with poor outcomes. MMP-9 (p = 0.086) and claudin-5 (p = 0.2) were not significantly associated with the outcome, although increased expressions of both markers were observed at 12 h. Multiple logistic regression showed that sST2 levels ≥71.8 ng/mL at 24 h, with a specificity of 96.9%, emerged as an independent predictor of poor functional outcome (OR: 6.44; 95% CI: 1.40-46.3; p = 0.029). CONCLUSION: Evaluation of sST2 may act as a reliable biomarker of functional outcome in 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 | 0.000 | 0.001 |
| 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) | 0.002 | 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".