Recent advances in genetic testing for familial hypercholesterolemia
Bibliographic record
Abstract
INTRODUCTION: Familial hypercholesterolemia (FH) is a common genetic cause of premature coronary heart disease that is widely underdiagnosed and undertreated. To improve the identification of FH and initiate timely and appropriate treatment strategies, genetic testing is becoming increasingly offered worldwide as a central part of diagnosis. Areas covered: Recent advances have been propelled by an improved understanding of the genetic determinants of FH together with substantially reduced costs of appropriate screening strategies. Here we review the various methods available for obtaining a molecular diagnosis of FH, and highlight the particular advantages of targeted next-generation sequencing (NGS) platforms as the most robust approach. Furthermore, we note the importance of screening for copy number variants and common polymorphisms to aid in molecularly defining suspected FH cases. Expert commentary: The need for genetic analysis of FH will increase, both for diagnosis and reimbursement of new therapies. An effective molecular diagnostic method must detect: 1) molecular and gene locus heterogeneity; 2) a wide range of mutation types; and 3) the polygenic component of FH. As availability of genetic testing for FH expands, standardization of variant curation, maintenance of clinical databases and registries, and wider health care provider education all assume greater importance.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".