Proximal aortic distensibility is an independent predictor of all-cause mortality and incident cardiovascular events in the multi-ethnic study of atherosclerosis
Bibliographic record
Abstract
Background: The predictive value of Ascending Aortic Distensibility (AAD) for mortality and Cardiovascular Disease (CVD) events has not been established in the general population. Our aim was to assess the utility of AAD to predict mortality and incident CVD events beyond conventional risk factors and Framingham CVD risk category in the Multi-Ethnic Study of Atherosclerosis (MESA) Methods: AAD was measured with magnetic resonance imaging at baseline in 3677 MESA participants free of overt CVD. Cox proportional-hazards regression was used to evaluate the risk of death and incident CVD in relation to AAD and the Framingham risk score. Results: There were 196 deaths and 224 CVD events over an average of 7.2 years of follow-up. Decreased AAD was associated with increased risk of all-cause mortality with a hazard ratio for the first vs. fifth quintile of AAD of 2.3 (95% CI: 1.1-4.7, p=0.02) independent of age, gender and other CV risk factors. Subjects with lowest AAD had a twofold risk of incident CVD independent of the Framingham risk score category (p=0.03). Decreased AAD was associated with incident CVD in low-to-intermediate CV risk individuals with a hazard ratio for the first quintile of AAD of 3.7 (95% CI: 1.1-13.1, p=0.03) independent of age, gender and other CVD risk factors. This adjusted relationship was not significant in high CVD risk individuals. Conclusions: Decreased proximal aorta distensibility is a significant predictor of all-cause mortality and incident CV events among individuals without overt CVD. AAD refines risk stratification amongst asymptomatic low-to-intermediate Framingham risk individuals.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".