Association between plasma lipoprotein levels and bioprosthetic valve structural degeneration
Bibliographic record
Abstract
INTRODUCTION: Structural valve degeneration (SVD) leads to the failure of aortic valve bioprostheses. It is suspected that lipid-derived factors could play a role in SVD. We hypothesised that oxidised low-density lipoprotein (OxLDL), OxLDL/LDL, OxLDL/high-density lipoprotein (OxLDL/HDL) and proprotein convertase subtilisin/kexin 9 (PCSK9) may be associated with SVD. METHODS: We included 199 patients who underwent an aortic valve replacement with a bioprosthesis and had an echocardiography follow-up to evaluate the function of the prosthesis. SVD was defined as an increase in mean transprosthetic gradient (≥10 mm Hg) or a worsening of transprosthetic regurgitation (≥1/3) during the follow-up. RESULTS: ) (p=0.02). In a model adjusted for covariates, only OxLDL/HDL (OR 1.49, 95%CI 1.08 to 2.07 per 10 units, p=0.01) remained associated with SVD. There was a significant interaction between OxLDL/HDL and PCSK9 on SVD (p=0.05). After adjustment, compared with patients with low OxLDL/HDL (median, <25.4) and low PCSK9 (median, <298 ng/mL) (referent), patients with both an elevated OxLDL/HDL ratio and PCSK9 had a higher risk of SVD (OR 2.93, 95% CI 1.02 to 9.29, p=0.04). CONCLUSIONS: OxLDL/HDL ratio is independently associated with SVD.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".