Evolving Treatments for Arterial and Venous Thrombosis
Bibliographic record
Abstract
The direct oral anticoagulants (DOACs) represent a major advance in oral anticoagulant therapy and have replaced the vitamin K antagonists as the preferred treatment for many indications. By simplifying long-term anticoagulant therapy and improving its safety, the DOACs have the potential to reduce the global burden of thrombosis. Postmarketing studies suggest that the favorable results achieved with DOACs in the randomized controlled trials can be readily translated into practice, but highlight the need for appropriate patient, drug and dose selection, and careful follow-up. Leveraging on their success to date, ongoing studies are assessing the utility of DOACs for the prevention of thrombosis in patients with embolic stroke of unknown source, heart failure, coronary artery disease, peripheral artery disease, antiphospholipid syndrome, and cancer. The purpose of this article is to (1) review the pharmacology of the DOACs, (2) describe the advantages of the DOACs over vitamin K antagonists, (3) summarize the experience with the DOACs in established indications, (4) highlight current challenges and limitations, (5) highlight potential new indications; and (6) identify future directions for anticoagulant therapy.
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 | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".