Incidence, risk factors, clinical impact, and management of bioprosthesis structural valve degeneration
Bibliographic record
Abstract
PURPOSE OF REVIEW: Structural valve deterioration is the major cause of bioprosthesis failure and is increasing over time. We present an overview of incidence, mechanisms, predictors, clinical impact, and management of bioprosthetic valve structural degeneration. RECENT FINDINGS: Early degeneration caused by calcification and destruction of connective tissue of the prosthesis is controlled by multiple mechanisms, from mechanical stress to infiltration of lipids and inflammatory cells, and activation of the immune system. Despite major improvements in valve design and surgical procedures, the pathology is still the main limiting factor to the long-term durability. Appropriate selection of the model and size of bioprosthesis as well as proper medical management and follow-up after valve replacement are essential for optimal prevention, detection, and management of structural valve deterioration. Currently, redo open-heart surgery is the most frequently used approach to treat structural valve deterioration. The transcatheter valve-in-valve procedure, however, is a valuable alternative to surgery for high-risk patients. SUMMARY: Structural valve deterioration is responsible for significant morbidity and mortality after valve replacement. This multifactorial pathology is the main cause of valve re-intervention during follow-up. Although redo surgery is still the most frequently used intervention to treat valve structural failure, the transcatheter valve-in-valve procedure is rapidly expanding.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".