Emerging anticoagulant strategies
Bibliographic record
Abstract
Despite the introduction of direct oral anticoagulants (DOACs), the search for more effective and safer antithrombotic strategies continues. Better understanding of the pathogenesis of thrombosis has fostered 2 new approaches to achieving this goal. First, evidence that thrombin may be as important as platelets to thrombosis at sites of arterial injury and that platelets contribute to venous thrombosis has prompted trials comparing anticoagulants with aspirin for secondary prevention in arterial thrombosis and aspirin with anticoagulants for primary and secondary prevention of venous thrombosis. These studies will help identify novel treatment strategies. Second, emerging data that naturally occurring polyphosphates activate the contact system and that this system is critical for thrombus stabilization and growth have identified factor XII (FXII) and FXI as targets for new anticoagulants that may be even safer than the DOACs. Studies are needed to determine whether FXI or FXII is the better target and to compare the efficacy and safety of these new strategies with current standards of care for the prevention or treatment of thrombosis. Focusing on these advances, this article outlines how treatment strategies for thrombosis are evolving and describes the rationale and approaches to targeting FXII and FXI. These emerging anticoagulant strategies should address unmet needs and reduce the systemic underuse of anticoagulation because of the fear of bleeding.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.007 |
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".