Optimizing the safety of treatment for venous thromboembolism in the era of the direct oral anticoagulants
Bibliographic record
Abstract
Direct oral anticoagulants (DOACs) are rapidly replacing vitamin K antagonists (VKAs) for treatment of venous thromboembolism (VTE). The DOACs include dabigatran, which inhibits thrombin, and rivaroxaban, apixaban, and edoxaban, which inhibit factor Xa. When compared with conventional VTE treatment consisting of a parenteral anticoagulant followed by a VKA, the DOACs were equally effective for prevention of recurrence, but were associated with less bleeding. With similar efficacy, better safety, and the convenience of fixed dosing without the need for routine coagulation monitoring, guidelines now recommend DOACs over VKAs for VTE treatment in patients without active cancer. Nonetheless, measures are needed to optimize the safety of DOACs. Focusing on these measures, this paper summarizes the results of phase III trials evaluating DOACs for VTE treatment; identifies which VTE patients are or are not candidates for DOACs; provides guidance on how to choose among DOACs; lists the licensed dosing information for DOACs; discusses the optimal treatment duration for VTE; describes periprocedural management of DOACs in patients requiring surgery or intervention; and finally, reviews the management of bleeding, including the role for specific reversal agents.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".