Clinical Implementation and Outcomes of Genetic Testing for Epilepsy by the Ontario Epilepsy Genetic Testing Program
Bibliographic record
Abstract
BACKGROUND: Epilepsy is a relatively common condition that affects approximately 4-5 per 1000 individuals in Ontario, Canada. While genetic testing is now prevalent in diagnostic and therapeutic care plans, optimal test selection and interpretation of results in a patient-specific context can be inconsistent and provider dependent. METHODS: The first of its kind, the Ontario Epilepsy Genetic Testing Program (OEGTP) was launched in 2020 to develop clinical testing criteria, curate gene content, standardize the technical testing criteria through a centralized testing laboratory, assess diagnostic yield and clinical utility and increase genetics literacy among providers. RESULTS: Here we present the results of the first two years of the program, demonstrating the overall 20.8% diagnostic yield including pathogenic sequence and copy number variation detected by next-generation sequencing panels. Routine follow-up testing of family members enabled the resolution of ambiguous findings. Post-test outcomes were collected as reported by the ordering clinicians, highlighting the clinical benefits of genetic testing. CONCLUSION: This programmatic approach to genetic testing in epilepsy by OEGTP, together with engagement of clinical and laboratory stakeholders, provided a unique opportunity to gather insight into province-wide implementation of a genetic testing program.
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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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".