Implementation of Epilepsy Multigene Panel Testing in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: Epilepsy is a common neurological condition that shows a marked genetic predisposition. The advent of next-generation sequencing (NGS) has transformed clinical genetic testing by allowing the rapid screen for causative variants in multiple genes. There are currently no NGS-based multigene panel diagnostic tests available for epilepsy as a licensed clinical diagnostic test in Ontario, Canada. Eligible patient samples are sent out of country for testing by commercial laboratories, which incurs significant cost to the public healthcare system. OBJECTIVE: An expert Working Group of medical geneticists, pediatric neurologists/epileptologists, biochemical geneticists, and clinical molecular geneticists from Ontario was formed by the Laboratories and Genetics Branch of the Ontario Ministry of Health and Long-Term Care to develop a programmatic approach to implementing epilepsy panel testing as a provincial service. RESULTS: The Working Group made several recommendations for testing to support the clinical delivery of care in Ontario. First, an extension of community healthcare outcomes-based program should be incorporated to inform and educate ordering providers when requesting and interpreting a genetic panel test. Second, any gene panel testing must be "evidence-based" and takes into account varied clinical indications to reduce the chance of uncertain and secondary results. Finally, an ongoing evaluative process was recommended to ensure continued test improvement for the future. CONCLUSION: This epilepsy panel testing implementation plan will be a model for genetic care directed toward a specific set of conditions in the province and serve as a prototype for genetic testing for other genetically heterogeneous diseases.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".