A Historical Perspective on the Reversal of Anticoagulants
Bibliographic record
Abstract
There has been a landmark shift in the last several decades in the management and prevention of thromboembolic events. From the discovery of parenteral and oral agents requiring frequent monitoring as early as 1914, to the development of direct oral anticoagulants (DOACs) that do not require monitoring or dose adjustment in the late 20th century, great advances have been achieved. Despite the advent of these newer agents, bleeding continues to be a key complication, affecting 2 to 4% of DOAC-treated patients per year. Bleeding is associated with substantial morbidity and mortality. Although specific reversal agents for DOACs have lagged the release of these agents, idarucizumab and andexanet alfa are now available as antagonists. However, the efficacy of these reversal agents is uncertain, and complications, including thrombosis, have not been adequately explored. As such, guidelines continue to advise the use of nonspecific prohemostatic agents for patients requiring reversal of the anticoagulant effect of these drugs. As the indications for DOACs and the overall prevalence of their use expand, there is an unmet need for further studies to determine the efficacy of specific compared with nonspecific pro-hemostatic reversal agents. In this review, we will discuss the evidence behind specific and nonspecific reversal agents for both parenteral and oral anticoagulants.
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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 | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| 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".