Influential factors of vascular cognitive impairment due to small vessel disease and its correlation with serum high-sensitivity C-reactive protein
Bibliographic record
Abstract
Objective To investigate the influential factors of vascular cognitive impairment due to small vessel disease( VCI-SVD) and its correlation with serum high-sensitivity C-reactive protein( hs-CRP). Methods Totally256 patients with cerebral SVD were scored by Montreal Cognitive Assessment( MoCA) table and the level of glycosylated hemoglobin,blood lipid and hs-CRP were measured respectively. They were divided into VCI-SVD group( MoCA≤26) and no VCI-SVD group( MoCA 26),and the results were compared and analyzed. Results One hundred and thirty-five patients were classfied as VCI-SVD group,121 patients were classfied as no VCI-SVD group.The serum levels of hs-CRP were higher significantly in VCI-SVD group compared with no VCI-SVD group( P 0. 01). Glycosylated hemoglobin levels,systolic blood pressure,atrial fibrillation,smoking,alcohol consumption were significantly higher than those in no VCI-SVD group,while years of education were significantly lower than that that in no VCI-SVD group( P 0. 05 ~ 0. 01). Multivariate logistic regression analysis showed,after controlling for correlation confounding risk factors,glycosylated hemoglobin levels and high serum hs-CRP were independent risk factors for VCI-SVD( OR = 1. 447,95% CI: 1. 25- 5. 54,OR = 1. 587,95% CI: 1. 87- 5. 67,all P 0. 05). The correlation analysis showed that serum hs-CRP levels were negatively correlated with MoCA total score and visualspatial and executive function,language,and short-term memory impairment( r =- 0. 621-- 0. 430,all P 0. 05),and had no correlation with name, abstract, orientation and attention items score. Conclusions Diabetes,hypertension,atrial fibrillation and smoking,drinking,years of education and the level of hs-CRP are the affecting factors of VCI-SVD,and diabetes and high serum hs-CRP are independent risk factors for VCI-SVD.
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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 | 0.001 |
| 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 | 0.000 | 0.001 |
| 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".