Analysis of Serum Inflammatory Markers in Cognitive Impairment among Acute Ischaemic Stroke Patients.
Bibliographic record
Abstract
BACKGROUND: Previous studies have indicated that changes in the expression of certain inflammatory biomarkers are associated with cognitive impairment (CI), but only a limited number of studies were conducted in patients with acute ischemic stroke (AIS). The present study aimed to evaluate the potential association between serum levels of several inflammatory markers and cognitive impairment in AIS patients. These markers included interleukin 6 (IL-6), C-reactive protein (CRP), plasma fibrinogen, erythrocyte sedimentation rate (ESR), and white blood cell (WBC) count. SUBJECTS AND METHODS: All participants were prospectively recruited from the Department of Neurology, Clinical Center University of Sarajevo. A total of 100 patients with first-ever AIS were included in stroke group and 30 in the non-stroke control group. Each patient underwent a comprehensive cognitive assessment and laboratory tests (IL-6, CRP, fibrinogen, ESR and WBC) within the first three days of admission. Cognitive status was assessed using cognitive instruments: the Mini-Mental State Examination, the Montreal Cognitive Assessment, the Frontal Assessment Battery, and the Addenbrooke's Cognitive Examination-Revised. RESULTS: Female stroke patients with CI had higher levels of IL-6 compared to those without CI and controls (p<0.017). AIS patients with CI had significantly higher plasma fibrinogen (p<0.001) and CRP levels (p<0.001) than controls, whereas there was no significant difference in comparison with cognitively intact patients. There were no statistical differences in ESR or WBC count between groups. CONCLUSIONS: Of the inflammatory markers, only IL-6 levels were associated with CI in AIS patients. Measuring circulating IL-6 could be used as a screening test to identify all such patients.
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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 | 0.001 | 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".