Anticoagulation for prosthetic heart valves
Bibliographic record
Abstract
PURPOSE OF REVIEW: The efficacy of anticoagulation for valvular prostheses is the result of a delicate balance between the risk of thromboembolic (TE) events and bleeding. Here, we review data on anticoagulation for valve prostheses with a focus on clinical trials that address key unanswered questions. RECENT FINDINGS: There are several unanswered questions in the field of prosthetic valve anticoagulation, including: optimal TE prophylaxis in the short term for bioprostheses, optimal TE prophylaxis following transcatheter aortic valve implantation, the safety and efficacy of lower levels of anticoagulation with the bileaflet mechanical prosthesis, the role of aspirin for patients with mechanical prostheses, and the management of anticoagulation for mechanical valves in pregnancy. Other areas of study include the role, if any, of nonwarfarin oral anticoagulants for prosthetic TE prophylaxis as well as self-INR monitoring. Finally, we briefly mention studies of newer anticoagulants, such as novel vitamin K antagonists and antisense oligonucleotides, that are on the horizon. SUMMARY: Optimal antithrombotic management is a key issue for patients with valvular prostheses, and the publication of recent trials has provided much-needed guidance. We highlight areas of progress, in addition to the major unanswered questions for which well-designed, prospective clinical trials are forthcoming.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".