Atherothrombotic factors and atherosclerotic cardiovascular events: the multi-ethnic study of atherosclerosis
Bibliographic record
Abstract
AIMS: Traditional atherosclerotic cardiovascular disease (ASCVD) risk factors fail to address the full spectrum of the complex interplay of atherosclerotic and atherothrombotic factors integral to ASCVD events. This study sought to examine the association between atherothrombotic biomarkers and ASCVD events. METHODS AND RESULTS: The association between atherothrombotic biomarkers and 877 ASCVD events with and without adjustment for traditional risk factors was evaluated via Cox proportional hazards models and factor analysis in 5789 Multi-Ethnic Study of Atherosclerosis participants over a median follow-up of 14.7 years. Factor analysis accounted for multidimensional relationship and shared variance among study biomarkers, which identified two new variables: a thrombotic factor (Factor 1), principally defined by shared variance in fibrinogen, plasmin-antiplasmin complex, factor VIII, D-dimer, and lipoprotein(a), and a fibrinolytic factor (Factor 2), principally defined by shared variance of plasminogen and oxidized phospholipids on plasminogen. In a model including both factors, the thrombotic factor was associated with the higher risk of ASCVD events [hazard ratio (HR) 1.57, 95% confidence interval (CI) 1.45, 1.70], while the fibrinolytic factor was associated with the lower risk of ASCVD events (HR 0.76, 95% CI 0.70, 0.82), with estimated ASCVD free survival highest for low atherothrombotic Factor 1 and high atherothrombotic Factor 2. CONCLUSION: Two atherothrombotic factors, one representative of thrombotic propensity and the other representative of fibrinolytic propensity, were significantly and complementarily associated with incident ASCVD events, remained significantly associated with incident ASCVD after controlling for traditional risk factors, and have promise for identifying patients at high ASCVD event risk specifically due to their atherothrombotic profile.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".