Bridging therapy and oral anticoagulation: current and future prospects
Bibliographic record
Abstract
PURPOSE OF REVIEW: Patients undergoing oral anticoagulation treatment with vitamin K antagonist (VKA) therapy are at a high risk of bleeding when undergoing an invasive surgery or procedure. Bridging therapy with parenteral heparin, usually at therapeutic doses, aims to protect these patients against thromboembolism during temporary periprocedural interruption of VKA therapy. Whether or not to interrupt VKA therapy and initiate bridging therapy is a difficult decision that is based upon both the patient's and the procedure's thromboembolic and bleeding risks. RECENT FINDINGS: There are minor procedures that can safely be done without the need for VKA interruption. Patient groups that may benefit from bridging therapy during temporary VKA interruption for a procedure include those who are at moderate-to-high risk of thromboembolism. Procedural bleed risk should determine when to resume bridging and VKA therapies. Recent findings highlight that low-molecular-weight heparin, usually in the outpatient setting, is the preferred agent over intravenous unfractionated heparin for bridging therapy, which includes patients with prosthetic heart valve indications for VKA therapy. SUMMARY: Large, recently initiated placebo-controlled trials in bridging therapy are discussed, as well as future alternatives to VKA therapy in oral anticoagulation during the periprocedural period.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".