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 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 | 0.015 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".