The current landscape of treatment options for venous thromboembolism: a focus on novel oral anticoagulants
Bibliographic record
Abstract
BACKGROUND: Venous thromboembolism (VTE), comprising deep vein thrombosis (DVT) and pulmonary embolism (PE), is a major cause of morbidity, mortality, and healthcare expenditure. Anticoagulant therapy is recommended for at least 3 months in patients with acute VTE to prevent recurrence. Conventional anticoagulants are associated with inherent limitations including route of administration, required monitoring and dose adjustments, potential for food-drug and drug-drug interactions, unpredictable pharmacokinetics and pharmacodynamics, and possible severe adverse events. SCOPE: This manuscript reviews the pharmacology of the novel oral anticoagulants (NOACs), and analyzes the differences in phase 3 clinical trial designs, outcomes, and specific patient populations investigated for the treatment of acute and prevention of secondary VTE. METHODS: A literature search was performed in PubMed using the key words dabigatran, apixaban, rivaroxaban, edoxaban, and venous thromboembolism in PubMed. The search included all years, English language, and peer-reviewed articles relating to phase 3 clinical trials, subanalyses, and meta-analyses of these NOACs for the treatment of acute VTE and secondary prevention. FINDINGS: NOACs have demonstrated comparable efficacy and comparable or superior safety in large, randomized clinical trials in the treatment and prevention of VTE compared with conventional therapy. New oral anticoagulants, including the direct thrombin (dabigatran etexilate) and direct factor Xa inhibitors (rivaroxaban, apixaban, and edoxaban), have advantages over conventional agents such as oral administration at fixed doses, predictable pharmacokinetics and pharmacodynamics, minimal potential for food-drug and drug-drug interactions, and lack of required monitoring. CONCLUSIONS: NOACs offer additional oral anticoagulation treatment options for patients with VTE.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".