RACIAL AND ETHNIC DIFFERENCES IN LONG-TERM CARDIOVASCULAR MORTALITY AMONG WOMEN AND MEN FROM THE CAC CONSORTIUM
Bibliographic record
Abstract
Abstract Background Despite an increasingly diverse population, knowledge regarding racial and ethnic disparities is limited among women and men undergoing atherosclerotic cardiovascular (ASCVD) screening. Our aim was to compare CV mortality by ASCVD risk and coronary artery calcium (CAC) scores among Black and Hispanic women and men compared to other participants. Design and Methods From the CAC Consortium, 42,964 participants with self-reported race and ethnicity were followed for a median of 11.7 years. Multivariable Cox proportional hazards regression models were used to estimate CV mortality, with separate analyses by sex. Results One-third of enrollees were women; 977 self-reported as Black, 1,349 as Hispanic, 1,621 as Asian, and 740 as American Indian/Native Alaskan/Hawaiian or other; the remainder were white. Black women and men had higher ASCVD risk and CAC scores yielding the highest CV mortality compared to other participants. Among Black women and men with a 0 CAC or ASCVD risk score <5%, hazard ratios (HRs) were 6-9-fold higher than that of other women and men. In men with CAC scores ≥100, Black men (HR: 4.2,p<0.001) had the highest CV mortality compared to all other men. A similar high-risk pattern was noted for Black women with CAC scores ≥100 (p<0.001), even when adjusting for the ASCVD risk score. Overall, Hispanics had an elevated CV mortality, higher than others but less than that of Black participants. Patterns of intermediate risk were notable for Hispanic men with a CAC score of 0 (HR=3.6, p=0.006) and ≥100 (HR=2.3, p=0.03). Conclusions The disproportionately high and excess CV mortality among Black women and men represents significant barriers to reducing the burden of ASCVD through effective screening using ASCVD risk and CAC scores.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".