Atherothrombotic and thrombolytic biomarkers in incident stroke and atrial fibrillation-related stroke: The Multi-Ethnic Study of Atherosclerosis (MESA)
Bibliographic record
Abstract
Background and aims Although several biomarkers have been studied in thromboembolic stroke, measuring the balance between thrombus formation and thrombolysis and its role in predicting stroke and atrial fibrillation (AF)-related stroke is limited. We sought to assess atherothrombotic biomarkers grouped into composite factors that reflect thrombotic and thrombolytic potential, and the balance between these factors as it relates to incident stroke or transient ischemic attack (TIA) and stroke/TIA in AF. Methods A Thrombotic Factor , derived from fibrinogen, plasmin–antiplasmin complex, factor VIII, D-dimer, and lipoprotein(a); and a Thrombolytic Factor , derived from plasminogen and oxidized phospholipids on plasminogen, were evaluated at baseline in 5,764 Multi-Ethnic Study of Atherosclerosis (MESA) participants. We evaluated the association between these two factors representative of thrombotic and thrombolytic potential and incident stroke/TIA (n = 402), and AF-related stroke/TIA (n = 82) over a median of 13.9 and 3.7 years, respectively. Cox proportional hazard models adjusted for medication use, cardiovascular risk factors and CHA 2 DS 2 -VASc score were utilized. Harrell's C-index was estimated to evaluate model performance. Results In models including both factors, Thrombotic Factor was positively while Thrombolytic Factor was inversely associated with incident stroke/TIA and AF-related stroke/TIA. Incorporating these factors along with the CHA 2 DS 2 -VASc in adjusted models resulted in a small improvement in risk prediction of incident stroke/TIA and AF-related stroke/TIA compared to models without the factors (C-index from 0.697 to 0.704, and from 0.657 to 0.675, respectively). Conclusions Composite biomarker factors, representative of the balance between thrombotic and thrombolytic propensity, provided an improvement in predicting stroke/TIA beyond CHA 2 DS 2 -VASc score.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".