Perioperative anticoagulant management in patients with atrial fibrillation: practical implications of recent clinical trials
Bibliographic record
Abstract
Defining the safest perioperative anticoagulation management approach for patients who are receiving chronic anticoagulant therapy stroke prevention has been a challenging and longstanding dilemma, especially for patients with atrial fibrillation who constitute the most common patient group receiving long term anticoagulation. Using a case-based format, we summarize the findings of recent clinical trialswhich have helped to informed best practices for perioperative anticoagulant management in patients with atrial fibrillation and provide an algorithmic management approach to this problem. We have done so by exploring the evidence to address 3 key questions: Is it necessary to interrupt anticoagulation for a procedure? How to estimate a patient's risk for perioperative thromboembolism and bleeding? If chronic anticoagulation interruption is required, is bridging anticoagulation with heparin needed?
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".