Antithrombotic Management in Ischemic Stroke with Essential Thrombocythemia: Current Evidence and Dilemmas
Bibliographic record
Abstract
Thrombotic diseases like ischemic stroke are common complications of essential thrombocythemia (ET) due to abnormal megakaryopoiesis and platelet dysfunction. Ischemic stroke in ET can occur as a result of both cerebral arterial and venous thrombosis. Management of ET is aimed at preventing vascular complications including thrombosis. Acute management of ischemic stroke in ET is the same as that in the general population without myeloproliferative disorders. However, an ET patient with ischemic stroke is at high risk for rethrombosis and is therefore additionally managed with cytoreductive therapy and antithrombotic agents. Given abnormal platelet production in ET, there is suboptimal suppression of platelets with the standard recommended dose of aspirin for cardiovascular (CV) prevention. Hence, for optimal CV protection in ET, low-dose aspirin is recommended twice daily in an arterial thrombotic disease like atherothrombotic ischemic stroke in presence of the following risk factors: age >60 years, Janus kinase2 V617F gene mutation, and presence of CV risk factors. In the presence of the same risk factors, concurrent antiplatelet and anticoagulant therapy is suggested for venous thrombosis. However, increased risk of bleeding with dual antithrombotic agents poses a significant challenge in their use in cerebral venous thromboembolism or atrial fibrillation in presence of the above-mentioned risk factors. We discuss these dilemmas regarding antithrombotic management in ischemic stroke in ET in this case-based review of literature in the light of current evidence.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".