Predicting cardiovascular disease in familial hypercholesterolemia
Bibliographic record
Abstract
PURPOSE OF REVIEW: Familial hypercholesterolemia is a frequent genetic disease associated with a high lifetime risk of cardiovascular disease (CVD). Statins are the cornerstone of treatment of familial hypercholesterolemia; however, with the advent of novel LDL-cholesterol lowering therapies, it has become necessary to identify familial hypercholesterolemia subjects presenting a significant residual CVD risk. The aim of this review is to provide an update on the recent literature concerning cardiovascular risk stratification in familial hypercholesterolemia. RECENT FINDINGS: Recently, several clinical and genetic factors have been shown to be independent predictors of CVD in familial hypercholesterolemia. These include clinical scores such as the Montreal-FH-SCORE, novel protein biomarkers, carotid plaque score and genetic predictors such as genetic risk scores as well as single-nucleotide polymorphisms. SUMMARY: Although there has been recent progress in cardiovascular risk stratification in familial hypercholesterolemia, there is still a need to further refine our knowledge concerning phenotype modifiers in this disease. Indeed, current known predictors do not explain the entirety of cardiovascular risk. More precise individual risk stratification in familial hypercholesterolemia could help to better tailor the proper therapy for each patient.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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) | 0.002 | 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".