La prévalence et la distribution des maladies génétiques au Québec
Bibliographic record
Abstract
The prevalence and distribution of genetic diseases in the province of Quebec has been influenced by its population history. The current French Canadian population stems from 8,500 pioneers who left France for Nouvelle-France between 1608 and 1759. After the English conquest of Nouvelle-France in 1759, the French Canadian population remained mostly genetically isolated, for linguistic, cultural, and religious reasons. The migration of a small number of French individuals to Nouvelle-France created a founder effect. Subsequent migrations inland have created smaller regional founder effects. The limited size of the population favoured genetic drift, and the social context encouraged endogamy, i.e. unions between French Canadians with little admixture with English and other immigrants. Founder effects, genetic drift, and endogamy have all played a role in the current prevalence and distribution of genetic diseases now found in Quebec. The prevalence and distribution of genetic diseases in Quebec need to be taken into account in clinical practice. When clinicians are knowledgeable about the genetic diseases prevalent in the population they treat, they know to consider these diseases in differential diagnoses when appropriate and prioritize investigations accordingly. When developing a new diagnostic test for a genetic disease, the prevalence of the disease and the nature of the mutations found in the target population need to be taken into account. The performance of the test will depend on how well it accounts for the particularities of the disease in that population. In other words, how well does it detect the mutations found in that population? Interpretation of individual genetic test results will also depend on how well the test is expected to perform in the individual's population.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".