Bioprosthetic Valve Thrombosis
Bibliographic record
Abstract
Degenerative valve disease is on the rise with greater than 100,000 valve operations performed in the US alone per year. The majority of those procedures employ tissue bioprostheses to avoid the attendant risk of anticoagulation, especially in the elderly. Though traditionally this approach has been considered a superior option to avoid anticoagulation, more recent analyses have demonstrated a significant incidence of previously unrecognized thrombosis associated with bioprosthetic valves, especially with the more recent advent of the transcatheter aortic valve replacement implantations. Bioprosthetic valve thrombosis is a major cause of either acute or indolent bioprosthetic valve degeneration, and often has an elusive presentation causing delayed recognition and treatment. The literature has extensively addressed the risks and benefits of anticoagulation following bioprosthetic valve replacement to prevent bioprosthetic valve thrombosis (BPVT), without conclusive evidence-based recommendations. The duration of anticoagulation following an episode of BPVT is unclear, and lifelong anticoagulation has been suggested. The increasing use of transcatheter aortic valve replacement as an alternative to surgical aortic valve replacement in various risk groups has introduced new challenges with regards to valve thrombosis, which have been poorly studied with regards to optimal treatment and prevention. The increasing use of valve-in-valve procedures is expected to bring on further uncharted challenges.
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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 | 0.000 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".