State-of-the-Art Mini Review: Dual-Pathway Inhibition to Reduce Arterial and Venous Thromboembolism
Bibliographic record
Abstract
Venous thromboembolism (VTE) and arterial thromboembolism (ATE) are linked by the common mechanism of thrombin generation. Historically these entities have been treated as separate pathophysiologic processes requiring different treatments: VTE, as the formation of fibrin-/coagulation-factor-derived thrombus in low-flow vasculature, requiring anticoagulants; versus ATE, as largely platelet-derived thrombus in high-flow vasculature, requiring antiplatelet agents. Observational studies have elucidated shared risk factors and comorbidities predisposing individuals with VTE to ATE, and vice versa, and have bolstered the strategy of dual-pathway inhibition (DPI)-the combination of low-dose anticoagulants with antiplatelet agents-to reduce thrombotic outcomes on both sides of the vasculature. Randomized clinical trials have evaluated the efficacy and safety of such regimens-mostly rivaroxaban and aspirin-in high-risk groups of patients, including those with recent acute or chronic coronary syndrome, as well as those with peripheral artery disease with or without revascularization. Studies of extended VTE prophylaxis in acutely ill medical patients have also contributed to the evidence evaluating DPI. The totality of available data supports the concept that DPI can reduce major and fatal thromboembolic outcomes, including stroke, myocardial infarction, VTE, and cardiovascular death in key patient cohorts, with acceptable risk of bleeding. Further data are needed to refine which patients derive the best net clinical benefit from such an approach. At the same time, other novel agents such as contact pathway inhibitors that reduce thrombin generation without affecting hemostasis-and thus maximize safety-should be assessed in appropriate populations.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".