Genetic Testing for Familial Hypercholesterolemia: The Current State of Its Implementation in Canada
Bibliographic record
Abstract
Background: Familial hypercholesterolemia (FH) is a common genetic disorder, yet it remains largely underdiagnosed in Canada. Multiple national and international guidelines recommend the use of clinical genetic testing for FH. However, the level of its accessibility and use within Canada is unclear. This study aims to describe the current state of clinical FH genetic testing in Canada and barriers to its implementation. Methods: We conducted a cross-sectional survey of 23 genetic counsellors across 8 provinces, through the Canadian Association of Genetic Counsellors Cardiac Communities of Practice, to obtain information about the accessibility of genetic testing for FH and the use of genetic-counselling services. Results: Responses were obtained from 12 genetic counsellors (52%). Of the 8 provinces surveyed, clinical FH genetic testing is available in 7, with British Columbia being the exception. The Simplified Canadian Definition for FH is the diagnostic criterion most commonly utilized to determine genetic-testing eligibility, and it is used in 5 of the 8 provinces. Notably, the referral rate to genetic counsellors typically is low, with most genetic-counselling programs receiving ≤ three referrals per site per month. Quebec is the only province to report a higher rate of genetic-counsellor referrals for FH. Conclusions: Clinical FH genetic testing is not available widely in Canada and its implementation varies significantly by province; this includes the eligibility criteria to qualify for testing as well as the utilization of genetic counsellors. A harmonized national approach to FH diagnosis could improve the rates of diagnosis and treatment.
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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 | 0.013 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".