Association between serum cystatin C level and post‐stroke cognitive impairment in patients with acute mild ischemic stroke
Bibliographic record
Abstract
BACKGROUND: Mild ischemic stroke (MIS) has been proved to be closely related to post-stroke cognitive impairment (PSCI). However, there are relatively few studies on the risk factors of MIS. We aimed to evaluate the relationship between serum cystatin C (CysC) level and cognitive function in patients with acute MIS. METHODS: Four hundred consecutive patients with acute MIS were screened and 281 patients were eligible for this study. The serum CysC levels were detected within 24 h after admission. Cognitive function was assessed by Montreal Cognitive Assessment (MoCA) at 3 months after acute MIS. Logistic regression was used to identify the predictors of PSCI, and the receiver operating characteristic (ROC) curve was applied to explore the optimal cut-off value. RESULTS: One hundred sixty-four (58.4%) patients were diagnosed with PSCI at 3 months follow-up. The serum CysC levels in patients with PSCI were significantly higher than patients without PSCI (p < .001). The binary logistic regression analysis showed that higher serum CysC level was an independent predictor for PSCI at 3 months (odds ratio [OR], 5.745; 95% confidence interval, [CI], 1.089-30.311; p = 0.039). The ROC curve showed that area under the curve (AUC) was 0.723, and at a 0.945 mg/l CysC cut-off point, the sensitivity and specificity for PSCI at 3 months were 79.9% and 58.1%, respectively. CONCLUSION: Our findings suggested that the serum CysC levels were increased after acute MIS, and higher serum CysC levels at baseline might be an independent risk factor for PSCI in patients with acute MIS, but further research are warranted.
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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) | 0.000 | 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 | 0.001 | 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".