Correlation Between Cognitive Impairment and Homocysteine and S100B Protein in Patients with Progressive Ischemic Stroke
Bibliographic record
Abstract
Purpose: This study aimed to investigate the relationship between cognitive impairment and homocysteine (Hcy) and S100B protein in patients with progressive ischemic stroke (PIS). Patients and Methods: A total of 158 patients with PIS hospitalized in the Department of Neurology in Taixing People's Hospital from January 2020 to March 2022 were enrolled in the study. After 90 days of follow-up, the patients were divided into two groups according to the MoCA score-99 cases with cognitive impairment group (observation group) and 59 cases with cognitive normal group (control group). Causal diagram was drawn to assess the association between risk factors and PIS with observation group. The risk factors indicators of cognitive impairment in patients with PIS were screened. The related predictive indicators were screened by multivariate logistic regression analysis, and Pearson correlation analysis. The predictive value was analyzed by Receiver Operating Characteristic (ROC) curve. Results: Multivariate logistic regression analysis showed that age, hypertension, lesion position, Hcy and S100B protein were related risk factors for cognitive impairment in patients with PIS. Pearson correlation analysis was conducted between Hcy and S100 B protein and MoCA score, and revealed that Hcy and S100 B protein were negatively correlated with MoCA score. ROC curve analysis showed that the Area Under the Curve (AUC) of S100 B protein and Hcy in identifying cognitive impairment after PIS was 0.709 and 0.673, respectively, and the combined AUC of Hcy and S100B protein in predicting cognitive impairment after PIS was 0.739. Conclusion: Hcy and S100B protein are related risk factors for cognitive impairment in patients with PIS, and may be used as in a prediction model to predict cognitive impairment after PIS in the future.
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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".