Association between coronary artery calcification and cognitive function in a Chinese community-based population.
Bibliographic record
Abstract
BACKGROUND: Coronary atherosclerosis and cognitive impairment are both age-related diseases, with similar risk factors. Coronary artery calcium (CAC), a marker of coronary atherosclerosis, may play a role in early detection of individuals prone to cognitive decline. This study aimed to investigate the relationship between CAC and cognitive function, and the capability of CAC to identify participants with a high risk of dementia in a Chinese community-based population. METHODS: A total of 1332 participants, aged 40-80 years and free of dementia from a community located in Beijing were included. All participants completed neurocognitive questionnaires and noncontrast CT examinations. Cognitive performance tests (including verbal memory, semantic fluency, executive function, and global cognitive function tests), the Cardiovascular Risk Factors, Aging, and Incidence of Dementia (CIDE) risk score, and the CAC score (CACS) were evaluated by questionnaires and CT. A CAIDE score ≥ 10 was considered to indicate a high risk of dementia in late-life. Participants were divided into three groups according to CACS (0, 1-399, ≥ 400). RESULTS: < 0.001). CONCLUSIONS: CAC, a marker of subclinical atherosclerosis, was significantly associated with cognitive performance in verbal memory and global cognitive function. CAC had a moderate capability to identify participants with a high risk of dementia, independent of age, education, and other 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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".