Portrait of autosomal recessive diseases in the <scp>French‐Canadian</scp> founder population of <scp>Saguenay‐Lac‐Saint‐Jean</scp>
Bibliographic record
Abstract
The population of the Saguenay-Lac-Saint-Jean (SLSJ) region, located in the province of Quebec, Canada, is recognized as a founder population, where some rare autosomal recessive diseases show a high prevalence. Through the clinical and molecular study of 82 affected individuals from 60 families, this study outlines 12 diseases identified as recurrent in SLSJ. Their carrier frequency was estimated with the contribution of 1059 healthy individuals, increasing the number of autosomal recessive diseases with known carrier frequency in this region from 14 to 25. We review the main clinical and molecular features previously reported for these disorders. Five of the studied diseases have a potential lethal effect and three are associated with intellectual deficiency. Therefore, we believe that the provincial program for carrier screening should be extended to include these eight disorders. The high-carrier frequency, together with the absence of consanguinity in most of these unrelated families, suggest a founder effect and genetic drift for the 12 recurrent variants. We recommend further studies to validate this hypothesis, as well as to extend the present study to other regions in the province of Quebec, since some of these disorders could also be present in other French-Canadian families.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".