A NEW NON-INVASIVE AND BRIEF CVD RISK SCORE SYSTEM TO PREDICT COGNITIVE DECLINE
Bibliographic record
Abstract
The main goal of our study was to assess the impact of a modified CAIDE risk score on cognitive performance in community-dwelling older adults. The study evaluated a multicultural population consisting of 314 participants enrolled in a cross-sectional aging study with valid measures on cognitive, physical, functional, and emotional health. The CAIDE risk score system was modified to: 1) reflect age and education levels specific to an older adult sample; 2) incorporate self-reported high cholesterol instead of total cholesterol level from blood test; and 3) use the Mini Physical Performance Test (mPPT) as a proxy measure of physical activity (mPPT:<12 inactive; >=12 active). Using hierarchical linear regression models, ‘risk’ of cognitive impairment (MoCA<26) was assessed based on levels of CAIDE risk score (low, intermediate, high) using the equations reported by the authors of the CAIDE risk score. Higher modified CAIDE risk scores were significantly associated with lower MoCA score (β=-0.447, p<0.001) and with higher Framingham vascular risk scores (r=0.673, p<0.001). The association was robust remaining significant after controlling for significant risk factors (β=-0.424, p=0.015) and followed a dose-response pattern. Likelihood of cognitive impairment increased from 12.1% in the low risk group, to 35.8% in the intermediate, to 65.5% in the high-risk group. Findings suggest the utility of using the modified CAIDE risk score as an indicator of increased risk of cognitive impairment and highlight the need for brief, easy to administer, non-invasive cardiovascular risk batteries to estimate risk of future cognitive decline and dementia in later life.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".