Carotid Ultrasound‐Based Plaque Score for the Allocation of Aspirin for the Primary Prevention of Cardiovascular Disease Events: The Multi‐Ethnic Study of Atherosclerosis and the Atherosclerosis Risk in Communities Study
Bibliographic record
Abstract
BACKGROUND: Coronary artery calcium testing using noncontrast cardiac computed tomography is a guideline-indicated test to help refine eligibility for aspirin in primary prevention. However, access to cardiac computed tomography remains limited, with carotid ultrasound used much more often internationally. We sought to update the role of aspirin allocation in primary prevention as a function of subclinical carotid atherosclerosis. METHODS AND RESULTS: The study included 11 379 participants from the MESA (Multi-Ethnic Study of Atherosclerosis) and ARIC (Atherosclerosis Risk in Communities) studies. A harmonized carotid plaque score (range, 0-6) was derived using the number of anatomic sites with plaque from the left and right common, bifurcation, and internal carotid artery on ultrasound. The 5-year number needed to treat and number needed to harm as a function of the carotid plaque score were calculated by applying a 12% relative risk reduction in atherosclerotic cardiovascular disease (ASCVD) events and 42% relative increase in major bleeding events related to aspirin use, respectively. The mean age was 57 years, 57% were women, 23% were Black, and the median 10-year ASCVD risk was 12.8%. The 5-year incidence rates (per 1000 person-years) were 5.5 (4.9-6.2) for ASCVD and 1.8 (1.5-2.2) for major bleeding events. The overall 5-year number needed to treat with aspirin was 306 but was 2-fold lower for individuals with carotid plaque versus those without carotid plaque (212 versus 448). The 5-year number needed to treat was less than the 5-year number needed to harm when the carotid plaque score was ≥2 for individuals with ASCVD risk 5% to 20%, whereas the presence of any carotid plaque demarcated a favorable risk-benefit for individuals with ASCVD risk >20%. CONCLUSIONS: Quantification of subclinical carotid atherosclerosis can help improve the allocation of aspirin therapy.
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".