Biomarkers of aortic bioprosthetic valve structural degeneration
Bibliographic record
Abstract
PURPOSE OF REVIEW: Bioprosthetic valves are now used for the majority of surgical aortic valve replacements and for all transcatheter aortic valve replacements. However, bioprostheses are subject to structural valve deterioration (SVD) and have, therefore limited durability. RECENT FINDINGS: Clinical, imaging, and circulating biomarkers may help to predict or indicate the presence of bioprosthetic valve SVD. The most important biomarkers of SVD includes: patient-related clinical biomarkers, such as diabetes and renal failure; valve-related biomarkers, such as absence of antimineralization process and severe prosthesis-patient mismatch; imaging biomarkers: the presence of valve leaflet mineralization on multidetector computed tomography or sodium fluoride uptake on positron emission tomography; and circulating biomarkers including: increased levels of HOMA index, ApoB/ApoA-I ratio, PCSK9, Lp-PLA2, phosphocalcic product. The assessment of these biomarkers may help to enhance risk stratification for SVD following AVR and may contribute to open novel pharmacotherapeutic avenues for the prevention of SVD. SUMMARY: SVD may affect all bioprostheses after aortic valve replacement, and is the main cause of bioprosthetic valve failure and reintervention during the follow-up. Comprehensive assessment of clinical, imaging, and circulating biomarkers associated with earlier SVD could help strengthen the follow-up in high-risk patients and provide novel pharmacologic therapeutic strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".