Population history and its impact on medical genetics in Quebec
Bibliographic record
Abstract
Knowledge of the genetic demography of Quebec is useful for gene mapping, diagnosis, treatment, community genetics and public health. The French-Canadian population of Quebec, currently about 6 million people, descends from about 8500 French settlers who arrived in Nouvelle-France between 1608 and 1759. The migrations of those settlers and their descendants led to a series of regional founder effects, reflected in the geographical distribution of genetic diseases in Quebec. This review describes elements of population history and clinical genetics pertinent to the treatment of French Canadians and other population groups from Quebec and summarizes the cardinal features of over 30 conditions reported in French Canadians. Some were discovered in French Canadians, such as autosomal recessive ataxia of the Charlevoix-Saguenay (MIM 270550), agenesis of corpus callosum and peripheral neuropathy (MIM 218000) and French-Canadian-type Leigh syndrome (MIM 220111). Other conditions are particularly frequent or have special genetic characteristics in French Canadians, including oculopharyngeal muscular dystrophy, hepatorenal tyrosinaemia, cystic fibrosis, Leber hereditary optic neuropathy and familial hypercholesterolaemia. Three genetic diseases of Quebec First Nations children are also discussed: Cree encephalitis (MIM 608505), Cree leukoencephalopathy (MIM 603896) and North American Indian childhood cirrhosis (MIM 604901).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".