Abstract 11975: The Multi-Ethnic Association of Aortic Valve Calcium and Long-Term Risk of Incident Severe Aortic Stenosis: Multi-Ethnic Study of Atherosclerosis
Bibliographic record
Abstract
Introduction: Racial/ethnic differences exist in the prevalence and burden of aortic valve calcium (AVC) and in the incidence of aortic stenosis (AS). While AVC is strongly associated with the long-term risk for AS, whether this association differs by race/ethnicity remains uncertain. Hypothesis: There will be a similar absolute event rate for incident moderate to severe AS within AVC categories across race/ethnicity. Methods: 6,812 MESA participants underwent non-contrast EKG gated CT at MESA Visit 1 and AVC was quantified using the Agatston scoring method. Adjudication of incident moderate and severe AS was defined using standard clinical criteria. We assessed the association of AVC with the combined outcome of incident moderate-severe AS stratified by race/ethnicity. We calculated the event rate per 1,000 person-years and multivariate Cox proportional hazards with log-transformed AVC as a continuous variable. Results: The mean age was 62 years old and 47% of participants were women. Over a median follow-up of 16.7 years, there were 140 participants diagnosed with moderate AS (n=56) and severe AS (n=84). The prevalence of AVC >0 by race was 15.8% for White, 13.3% for Hispanic, 12.3% for Black, and 8.3% for Chinese participants. The rate of incident moderate-severe AS was similar across race/ethnicity when stratified by AVC, p for interaction=0.62 (Figure 1). Log-transformed AVC had a similar association with the long-term risk of moderate-severe AS regardless of race/ethnicity: White HR 1.83 (95% CI 1.63-2.05), Hispanic: HR 2.12 (95% CI 1.79-2.51), Black: HR 2.31 (95% CI 1.79-2.98), and Chinese: HR 2.14 (95% CI 1.30-4.41). Conclusions: The association between AVC and moderate-severe AS was similar across race/ethnicity. This suggests that previously observed differences in AS by race/ethnicity may be predominantly due to the differing prevalence of AVC.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".