Bibliographic record
Abstract
Familial hypercholesterolemia (FH) is considered the genetic cause of coronary heart disease and ischemic stroke. FH is mainly an autosomal codominant pattern-based disorder and is primarily determined by point mutations within the low-density lipoprotein receptor, apolipoprotein B, and proprotein convertase subtilisin/kexin type 9 genes, causing increased low-density lipoprotein cholesterol levels in the serum of untreated individuals. The accumulation will eventually lead to atherosclerotic cardiovascular disease. Although clinical criteria comprising several prognosis scores, such as the Simon Broome, Dutch Lipid Clinic Network, Make Early Diagnosis to Prevent Early Death, and the recently proposed Montreal-FH-SCORE, are the conventional basis of diagnosing FH, the genetic diagnosis made by single nucleotide polymorphism genotyping, multiplex ligation-dependent probe amplification analysis, and sequencing (both Sanger and Next-Generation sequencing) offers unequivocal diagnosis. Given the heterogeneity of known mutations, the genetic diagnosis of FH is often difficult to establish, despite the growing evidence of the causative mutations, as well as the polygenic aspect of this pathology and the importance of cascade screening of the FH patient‚s healthy family members. This review article details different genetic techniques that can be used in FH identification when there is a clinical FH suspicion based on criteria comprised in prognosis scores, knowing that none of these are exhaustive in the diagnosis, yet they efficaciously overlap and complement each other for confirming the disease at the molecular level.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".