Anticoagulation reversal in the era of the non-vitamin K oral anticoagulants
Bibliographic record
Abstract
In recent years, non-vitamin K oral anticoagulants (NOACs) have emerged as an alternative to warfarin for the prevention and treatment of thrombo-embolic disease. Large randomized trials have demonstrated that these agents, which act by directly targeting thrombin (dabigatran) and factor Xa (rivaroxaban, apixaban, and edoxaban), are at least as effective as warfarin, with lower rates of bleeding and fewer interactions with food and drugs. In addition, NOACs have a more predictable anticoagulant effect, allowing a fixed dose regimen and obviating the need for routine anticoagulation monitoring. Since the introduction of NOACs, one of the major concerns for clinicians has been the lack of specific agents to reverse their anticoagulant effect in case of life-threatening haemorrhagic complications or emergency surgery, which have limited their use in patients deemed at a higher risk of bleeding. New specific antidotes (e.g. idarucizumab, andexanet alfa, and ciraparantag) show promising data, and may soon become available for clinical use. In this article, we review the pharmacology of these agents, the incidence and outcomes of haemorrhagic complications, the available strategies for anticoagulation reversal, and the more recent advances for the development of specific antidotes.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.002 |
| 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".