Correlation study of Framingham risk score and vascular dementia
Bibliographic record
Abstract
Vascular dementia (VaD) is one of the most common forms of dementia, and second only to Alzheimer's disease. The purpose of this study was to evaluate the potential diagnostic value of Framingham risk score (FRS) in VaD by investigating the relationship among cardiovascular risks, FRS, and VaD.Data were collected from patients (n = 130) at Tongji Hospital in Wuhan, China. They were divided into 2 groups, including the control group (n = 70) and the VaD group (n = 60). Statistical methods including t-test, logistic regression model, multiple linear regression model, and receiver-operating characteristic (ROC) curve were adopted for the assessment.A significant difference (all P < .05) was observed in systolic blood pressure (SBP), diastolic blood pressure (DBP), pulse pressure, total cholesterol (TC), homosysteine (HCY), glycosylated hemoglobin A1c (HbA1c), FRS, and cerebral white matter lesions (WMLs) between the 2 groups, even after adjusting for age (both P < .05). Age [odds ratio (OR) = 1.20; P = .002], FRS (OR = 1.55; P = .006), and WMLs (OR = 10.17; P = .011) were independent prognostic factors for VaD. The area under the ROC curve (AUC) of FRS for VaD diagnosis prediction was 0.830 (95% confidence interval, 95% CI: 0.730∼ 0.929). There was a significant difference in the AUC between WMLs and WMLs combined with FRS (0.788 (95% CI: 0.667 ∼ 0.880) versus 0.863 (95% CI: 0.754 ∼ 0.936, P = .049). Age, HbA1c, and FRS were negatively correlated with the mini-mental state examination (MMSE) scores (all P < .05) in the VaD group. Moreover, multiple stepwise linear regression analysis showed that the age and FRS were independent predictors of MMSE scores.FRS has a moderate predictive value for the VaD diagnosis, and also increases the risk of cognitive decline.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".