ANTITHROMBOTIC THERAPY IN PATIENTS WITH SURGICAL BIOPROSTHETIC AORTIC VALVE REPLACEMENT
Bibliographic record
Abstract
Aortic valve replacement (AVR) is the only life-saving treatment for patients with severe symptomatic aortic stenosis. Bioprosthetic valves are used in 90% of AVRs because they do not require lifelong anticoagulation. The major limitation of bioprosthetic valves is their limited durability compared to mechanical valves. In addition, bioprosthetic valves still carry a 2-3% risk of symptomatic valve thrombosis, stroke, and thromboembolism in the first 30 days after implantation, and a 1% annual risk thereafter. The risk of subclinical valve thrombosis is around 10% at 30 days and 25% at 1 year, and prevention of subclinical valve thrombosis is hypothesized to reduce the risk of clinical thrombotic events and perhaps even improve valve durability, although high-quality evidence is lacking. This doctoral thesis comprises 7 chapters of varied methodology that summarize the evidence behind current recommendations for antithrombotic therapy after bioprosthetic AVR, identify evidence gaps, and present the design a randomized trial that aims to address some of these evidence gaps. Chapter 1 introduces each included study with a brief summary. Chapter 2 is a narrative review summarizing guideline recommendation for antithrombotic therapy after bioprosthetic AVR and the evidence upon which they are based. Chapter 3 is an observational study describing antithrombotic prescribing practices in the VISION Cardiac Surgery cohort study. Chapter 4 is a systematic review and network meta-analysis of randomized studies of antithrombotic therapy after transcatheter aortic valve replacement. Chapter 5 is a systematic review and meta-analysis of randomized and observational studies of subclinical valve thrombosis. Chapter 6 presents the design and rationale of a feasibility trial of direct oral anticoagulants versus vitamin K antagonists in patients with a new surgical bioprosthetic AVR and atrial fibrillation. Chapter 7 discusses the implications, limitations, and future avenues of the research presented in this doctoral thesis.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".