Minimizing bleeding risk in patients receiving direct oral anticoagulants for stroke prevention
Bibliographic record
Abstract
Many primary care physicians are wary about using direct oral anticoagulants (DOACs) in patients with nonvalvular atrial fibrillation (AF). Factors such as comorbidities, concomitant medications, and alcohol misuse increase concerns over bleeding risk, especially in elderly and frail patients with AF. This article discusses strategies to minimize the risk of major bleeding events in patients with AF who may benefit from oral anticoagulant therapy for stroke prevention. The potential benefits of the DOACs compared with vitamin K antagonists, in terms of a lower risk of intracranial hemorrhage, are discussed, together with the identification of reversible risk factors for bleeding and correct dose selection of the DOACs based on a patient's characteristics and concomitant medications. Current bleeding management strategies, including the new reversal agents for the DOACs and the prevention of bleeding during preoperative anticoagulation treatment, in addition to health care resource use associated with anticoagulation treatment and bleeding, are also discussed. Implementing a structured approach at an individual patient level will minimize the overall risk of bleeding and should increase physician confidence in using the DOACs for stroke prevention in their patients with nonvalvular AF.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".