Coronary Artery Calcium: Associations with Brain Magnetic Resonance Imaging Abnormalities and Cognitive Status
Bibliographic record
Abstract
OBJECTIVES: To evaluate the association between coronary atherosclerosis and subclinical brain magnetic resonance imaging (MRI) abnormalities and between coronary atherosclerosis and abnormal cognitive function (dementia/mild cognitive impairment). DESIGN: Cross-sectional. SETTING: The Cardiovascular Health Study (CHS), an epidemiological study of risk factors for cardiovascular disease in older adults. PARTICIPANTS: Four hundred nine men and women, mean age 79, recruited from the Pittsburgh center of the CHS. MEASUREMENTS: Coronary atherosclerosis was defined according to the level of coronary artery calcification (CAC), as measured using electronic beam tomography. Subclinical brain MRI abnormalities included ventricular enlargement, white matter hyperintensities, and number of subcortical brain infarcts. Brain MRI and CAC measurements were performed between 1998 and 2000 at the Pittsburgh center of the CHS. Prevalence of brain MRI abnormalities and abnormal cognitive status were examined across quartiles of the CAC score, before and after controlling for age. Multivariate logistic regression models were used to assess whether CAC level was associated with abnormalities of brain MRI or abnormal cognitive status. RESULTS: Older adults with high CAC scores were more likely to have more-severe brain MRI abnormalities, including subcortical infarction and high white matter hyperintensities. The associations between CAC and ventricular enlargement showed a similar but not significant trend. The presence of any of the MRI abnormalities attenuated the association between CAC and abnormal cognitive status. CONCLUSION: Older adults with higher levels of CAC were more likely to have more-severe brain MRI abnormalities and abnormal cognitive status.
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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 | 0.004 |
| 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 | 0.001 | 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".