Familial Hypercholesterolemia and the Founder Effect Among Franco-Americans: A Brief History and Call to Action
Bibliographic record
Abstract
Familial hypercholesterolemia (FH) is an inherited disorder characterized by chronically elevated low-density lipoprotein cholesterol levels and an increased risk of premature atherosclerotic cardiovascular disease. FH has been shown to disproportionately affect French Canadians and other ethnic populations due to the presence of a founder effect characterized by reduced genetic diversity resulting from relatively few individuals with FH-causing genetic mutations establishing self-contained populations. Beginning in the mid-1800s, approximately 1 million French Canadians immigrated to the Northeastern United States and largely remained in these small, tight-knit communities. Despite extensive genetic- and population-based research involving the French-Canadian founder population, primarily in the Province of Quebec, little is known regarding Franco-Americans in the United States. Concurrent with addressing the underdiagnosis rate of FH in the general population, we propose the following steps to leverage this founder effect and meet the cardiovascular needs of Franco-Americans: (1) increase cascade screening in regions of the United States with a high proportion of individuals of French-Canadian descent; (2) promote registry-based, epidemiological research to elucidate accurate prevalence estimates as well as diagnostic and treatment gaps in Franco-Americans; and (3) validate contemporary risk stratification strategies such as the Montreal-FH-SCORE to enable optimal lipid management and prevention of premature atherosclerotic cardiovascular disease among French-Canadian descendants.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".