Successes of an innovative population-based carrier screening program for 4 prevalent recessive hereditary diseases in a population with a founder effect in Quebec, Canada
Bibliographic record
Abstract
Purpose: The Saguenay-Lac-Saint-Jean, Haute-Côte-Nord, and Charlevoix regions in Canada have a high prevalence of 4 autosomal recessive diseases with high morbidity and/or reduced life expectancy. As a result, a carrier screening program (CSP) was developed in 2010 and has been ongoing since. The program's purpose is to provide information and carrier screening for individuals having a higher probability to have an affected child to allow for informed decision making regarding reproductive choices. This publication provides an overview of the CSP, shares its results, and discusses the growing needs for expanding genetic testing and counseling. Methods: In 2018, the CSP transitioned from a regional to a provincially available program, supported by an innovative home self-sampling kit that can be requested online and returned by mail for analysis. For the purpose of this study, CSP data from 2010 to 2022 were extracted and analyzed. Results: = 116) were offered a subsequent appointment in a genetics clinic to further discuss the result, its potential implications, and available reproductive options. The heterozygote frequencies for the tested conditions ranged from 1 in 18 to 1 in 28. Conclusion: The relatively inexpensive method could be applied to other populations with a high prevalence of certain autosomal recessive diseases. Given the program's results, we must consider adding the screening of other prevalent recessive conditions to the CSP.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".