Antithrombotic therapy in aortic diseases: A narrative review
Bibliographic record
Abstract
Aortic diseases are a heterogeneous group of disorders, including atherothrombotic conditions like aortic atheroma, cholesterol embolization syndrome, aortic mural thrombus, thrombus within an aneurysm, and large vessel vasculitis. In this review, we provide a summary of the current evidence regarding atherothrombotic diseases of the aorta, focusing on therapeutic avenues. In patients with previous stroke, aortic arch atheroma is recognized as a strong predictor of recurrent atheroembolism, and antiplatelet therapy alone is still associated with a high (11.1%) residual risk of recurrent stroke. In secondary prevention, the use of dual antiplatelet therapy or moderate intensity anticoagulation with warfarin may lower the risk of recurrent stroke at a cost of increased life-threatening bleeding. Thrombi adherent to the aortic wall are generally associated with underlying atherosclerosis or aneurysmal disease. Primary aortic mural thrombus is a rare condition, sometimes related with systemic prothrombotic or inflammatory diseases. Retrospective studies suggest that anticoagulation is beneficial in patients with mobile mural thrombus. The pathogenesis and consequences of thrombus in an aortic aneurysm, or in an endograft following endovascular aneurysm repair, have been studied, but the role of antiplatelet therapy in those two conditions is still unclear and should be driven by general cardiovascular risk prevention. The benefit of anticoagulation to reduce thrombus load is uncertain. Patients with large vessel vasculitis experience increased cardiovascular events secondary to inflammation-driven atherothrombotic processes. Antiplatelet therapy is recommended as part of the therapy for prevention of cardiovascular disease. Anticoagulation with warfarin has shown limited benefit in few retrospective studies.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".