Anticoagulation in Patients with Heart Failure
Bibliographic record
Abstract
The decision to anti-coagulate patients with heart failure (HF) is a difficult one, with limited data available to support clinical judgment. Thromboembolic complications, both arterial (stroke) and venous (deep vein thrombosis and pulmonary embolism), remain a significant cause of mortality and morbidity in this population. The pathophysiology of thrombogenesis in HF may be contextualized in the classic triad of stasis, endothelial dysfunction and hypercoagulability. Dilated cardiac chambers, reduced systolic function, and left ventricular aneurysm or thrombus have been suggested as potential contributing factors. HF is associated with activation of inflammatory and neuroendocrine pathways, leading to endothelial dysfunction and a prothrombotic state with dysregulated platelets and activation of the coagulation cascade. The epidemiology of thromboembolic events in HF is poorly defined. Most studies are retrospective and include patients with concurrent atrial fibrillation. The current body of health outcomes research is reviewed to identify the specific etiological factors, prevalence, and impact of thromboembolic events in this patient population. Conflicting analyses exist regarding the risks and benefits of prophylaxis in HF. The data surrounding several classes of therapeutic agents are synthesized. Recent clinical trials on anticoagulation and HF are reviewed, including WATCH, WASH, and WARCEF. The absence of compelling clinical trial data leaves many unanswered questions regarding systemic anticoagulation in patients with HF.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".