Genetic Testing in Children with Epilepsy: Report of a Single-Center Experience
Bibliographic record
Abstract
BACKGROUND: Retrospective observational study to determine diagnostic yield and utility of genetic testing in children with epilepsy attending the Epilepsy Clinic at Children's Hospital, London, Ontario, Canada. METHODS: Children (birth-18 years) with epilepsy, who were seen in a 10-year period (January 1, 2008-March 31, 2018), were selected using defined inclusion criteria and by combining clinic datasets and laboratory records. RESULTS: In total, 105 children (52.38% male and 47.61% female) with a variety of seizures were included in the analysis. Developmental delay was documented in the majority (83; 79.04%). Overall, a genetic diagnosis was established in 24 (22.85%) children. The diagnostic yield was highest for whole-exome sequencing (WES), at 35.71%. The yield from microarray was 8.33%. Yields of single-gene testing (18.60%) and targeted multigene panel testing (19.23%) were very similar. Several likely pathogenic and pathogenic variants not previously reported were identified and categorized using ACMG criteria. All diagnosed patients underwent a review of anti-seizure medication management and received counseling on natural history of their disease, possible complications, recurrence risks, and possibilities of preimplantation or prenatal genetic diagnosis. CONCLUSIONS: Our study confirms the multiple benefits of detecting a genetic etiology in children with epilepsy. Similar yields in single versus multigene testing underscore the importance of accurate clinical phenotyping. Patients with epilepsy and their caregivers in Ontario would undoubtedly benefit from repatriation of multigene panels and WES to the province.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".