Overview of the New Oral Anticoagulants
Bibliographic record
Abstract
The non-vitamin K antagonist oral anticoagulants (NOACs) are replacing warfarin for many indications. These agents include dabigatran, which inhibits thrombin, and rivaroxaban, apixaban, and edoxaban, which inhibit factor Xa. All 4 agents are licensed in the United States for stroke prevention in atrial fibrillation and for treatment of venous thromboembolism and rivaroxaban and apixaban are approved for thromboprophylaxis after elective hip or knee arthroplasty. The NOACs are at least as effective as warfarin, but are not only more convenient to administer because they can be given in fixed doses without routine coagulation monitoring but also are safer because they are associated with less intracranial bleeding. As part of a theme series on the NOACs, this article (1) compares the pharmacological profiles of the NOACs with that of warfarin, (2) identifies the doses of the NOACs for each approved indication, (3) provides an overview of the completed phase III trials with the NOACs, (4) briefly discusses the ongoing studies with the NOACs for new indications, (5) reviews the emerging real-world data with the NOACs, and (6) highlights the potential opportunities for the NOACs and identifies the remaining challenges.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".