Next-Generation Anticoagulants: Precision Strategies for Patient-Centered Thromboprophylaxis
Bibliographic record
Abstract
Thrombosis remains a leading preventable cause of global morbidity and mortality, with conditions like venous thromboembolism and atrial fibrillation affecting millions worldwide. Traditional anticoagulants (heparins, vitamin K antagonists) require careful monitoring due to narrow therapeutic windows. Direct oral anticoagulants (DOACs) greatly improved convenience and reduced certain hemorrhagic complications (notably intracranial hemorrhage) compared to warfarin, but bleeding, drug-drug interactions, and unmet needs in special populations persist. This review highlights emerging strategies to decouple antithrombotic efficacy from bleeding risk. Novel agents targeting factor XI or XII (small molecules, antibodies, antisense oligonucleotides) have shown in early trials robust thromboembolism prevention with low bleeding. Advances in pharmacogenomics, biomarker-guided dosing, artificial intelligence risk prediction, and digital monitoring promise to personalize therapy. We discuss optimized approaches for high-risk subgroups (cancer-associated thrombosis, extremes of body weight, renal/hepatic dysfunction, pregnancy, perioperative care, and COVID-19) with citations to current evidence. Finally, we outline critical systems-level considerations, including drug accessibility, cost-effectiveness, and educational strategies, that are necessary to realize precision anticoagulation. Our synthesis is grounded in recent peer-reviewed literature and emphasizes innovations likely to improve safety and efficacy of thromboprophylaxis.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".