Organ-specific bleeding patterns of anticoagulant therapy: lessons from clinical trials
Bibliographic record
Abstract
Anticoagulants are effective at preventing and treating thrombosis, but can cause bleeding. For decades, vitamin K antagonists (VKAs) have been the only available oral anticoagulants. The development of non-VKA oral anticoagulants (NOACs), which inhibit either factor Xa or thrombin stoichiometrically, has provided alternatives to VKAs for several indications. The results of recent large-scale randomised controlled trials comparing NOACs with VKAs for the prevention of stroke in patients with non-valvular atrial fibrillation (AF) have produced some unexpected results. As a group, NOACs showed similar efficacy as warfarin, but a reduced risk of major bleeding. The reduction in bleeding with NOACs was greatest with intracranial hemorrhage. In contrast, the relative risk of gastro-intestinal bleeding was increased with some NOACs. In this review, we explore the potential mechanisms as well as the implications of these organ-specific bleeding patterns.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".