The role of blood protein biomarkers in acute ischemic stroke prognosis
Bibliographic record
Abstract
Abstract Background Stroke is one of the most frequent causes of mortality and disability, blood protein biomarkers are used to determine patients at high risk for a severe illness and to estimate the outcome. This study aimed to detect the relation between serum levels of C-reactive protein, matrix metalloproteinase 9, S100 calcium-binding protein B, brain natriuretic peptide, D-dimer and stroke severity and outcome in acute ischemic stroke patients. Results One hundred eighty-six patients with acute ischemic stroke participated and were subjected to complete general, neurological examination, assessment of stroke severity clinically and radiologically using National Institute of Health Stroke Scale (NIHSS), and Alberta Stroke Program Early CT (ASPECT) score and assessment of functional outcome using (modified Rankin Scale). C-reactive protein, matrix metalloproteinase 9, S100 calcium-binding protein B, brain natriuretic peptide and D-dimer were assessed. Higher C-reactive protein was found in patients with ASPECT score ≤ 7 and in patients with cerebral edema, seizures and was positively correlated with stroke severity according to NIHSS and modified Rankin Scale. C-reactive protein serum level at onset was negatively correlated with NIHSS at onset and is a significant predictor for mortality. D-dimer was negatively correlated with NIHSS. S100 calcium-binding protein B was significantly elevated in patients who developed hemorrhagic transformation. Conclusions Serum C-reactive protein level can be used as a predictor for mortality and higher S100 calcium-binding protein B was detected in patients with hemorrhagic transformation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.000 |
| 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".