Bleeding risk and the management of bleeding complications in patients undergoing anticoagulant therapy: focus on new anticoagulant agents
Bibliographic record
Abstract
For more than 60 years, heparin and coumarin have been mainstays of anticoagulation therapy. They are widely available, inexpensive, effective, and have specific antidotes but are regarded as problematic because of their need for careful monitoring. In addition, coumarin has a delayed onset of action, interacts with many medications, has a narrow therapeutic window, and is paradoxically prothrombotic in certain settings (ie, can precipitate "coumarin necrosis"). Heparin may require monitoring of its therapeutic effect and can also cause thrombosis (heparin-induced thrombocytopenia/thrombosis syndrome). These limitations have led to the development of new anticoagulants with the potential to replace current agents. These newer agents fall into 2 classes, based on whether they are antithrombin dependent (low-molecular-weight heparin, fondaparinux) or antithrombin independent (direct inhibitors of factor Xa and thrombin [factor IIa]). This paper addresses newer anticoagulants, reviewing their efficacy and limitations, and focuses on the risk of major bleeding that may complicate their use. In contrast to heparin and coumarin, none of these newer agents has a specific antidote that completely reverses its anticoagulant effect. Available data on the efficacy and safety of current and experimental agents for anticoagulant reversal are reviewed, and a plan for management of anticoagulant-induced bleeding is presented.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".