P.074 An assessment of next-generation panel testing in epilepsy
Bibliographic record
Abstract
Background: With the now routine use of next-generation sequencing it is important to know the baseline outcomes as they relate to clinical care for pediatric epilepsy in Ontario. We sought to assess the diagnostic yield of genetic epilepsy panel testing and characterize the impact on patient care. Methods: We conducted a retrospective chart review of patients with epilepsy seen at CHEO between 2012-2020 with genetic testing. 227 patients met our inclusion criteria. Patient charts were reviewed for clinical details, co-morbidities, genetic testing results, and changes to management. Results: Diagnostic yield was 19% for multi-gene epilepsy panel testing. A further 10% received a diagnosis from additional genetic testing. The diagnostic yield was significantly higher in patients with a younger age of onset of seizures. A direct change in clinical management as a result of the molecular diagnosis was evident for 9% of patients; however, all diagnoses impacted prognosis and family counselling. Conclusions: The diagnostic yield of genetic epilepsy panel testing conducted at CHEO is comparable to other reported rates. Genetic testing resulted in clinical benefits of recurrence risk counselling, prognostic information and though a direct change in management was advised in a minority of individuals, targeted treatment recommendations will continue to increase with ongoing testing.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".