Analysis of Risk Factors for Vascular Cognitive Impairment without Dementia
Bibliographic record
Abstract
Objective: To investigate the relationship between the vascular cognitive impairment without dementia (VCIND) and the risk factors, and the diagnosis and therapy thereof. Methods: The patients had the risk factors of ischemic cerebrovascular disease were collected from either the ward or clinic in Tianjin general hospital from April 2009 till August 2009. All the patients were detected the blood pressure and completed some related chemical and image examinations. The patients were divided into 2 groups by the clinical dementia ratio scale (CDR), VCIND group and the control group. The incidence of the risk factors was compared between the 2 groups and the relationship between VCIND and each risk factor was analyzed. All the patients were also divided into the high risk group and the low risk group according to the ESSEN stroke risk score. The score of Montreal cognitive assessment (MoCA) was then compared between these groups. Results:(1)Compared with the control group, the incidence of diabetes mellitus, hypercholesteremia, hypertriglyceridemia, hyperhomocytinemia, major artery stenosis and white matter lesion was obviously higher in VCIND group (P 0.05). However, there were no significant differences in age, the education years and the ration of gender between two groups. (2) Multivariable logistic regression analysis showed that diabetes mellitus, hyperhomocytinemia and white matter lesion had correlation with VCIND(P 0.05).(3)The score of MoCA was significantly lower in high risk group of ESSEN than that of low risk group (P 0.05). Conclusion: There was significant correlation between these risk factors(mellitus, hyperhomocytinemia and white matter lesion) and VCIND. There will be higher risk of cognitive impairment in patients with multiple risk factors.
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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) | 0.002 | 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".