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Record W1988513328 · doi:10.1051/medsci/20072311997

La prévalence et la distribution des maladies génétiques au Québec

2007· article· fr· W1988513328 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

Venuemédecine/sciences · 2007
Typearticle
Languagefr
FieldMedicine
TopicFolate and B Vitamins Research
Canadian institutionsCentre Hospitalier Universitaire Sainte-Justine
Fundersnot available
KeywordsEndogamyFounder effectPopulationGenetic driftImmigrationDemographyContext (archaeology)GenealogyEthnologyGeographyDistribution (mathematics)HistoryGeneticsSociologyGenetic variationBiologyHaplotypeGenotype

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.159
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.006
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.045
GPT teacher head0.389
Teacher spread0.345 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it