Diagnostic yield of genetic testing in epileptic encephalopathy in childhood
Bibliographic record
Abstract
OBJECTIVE: Epilepsy is a common neurologic disorder of childhood. To determine the genetic diagnostic yield in epileptic encephalopathy, we performed a retrospective cohort study in a single epilepsy genetics clinic. METHODS: We included all patients with intractable epilepsy, global developmental delay, and cognitive dysfunction seen between January 2012 and June 2014 in the Epilepsy Genetics Clinic. Electronic patient charts were reviewed for clinical features, neuroimaging, biochemical investigations, and molecular genetic investigations including targeted next-generation sequencing of epileptic encephalopathy genes. RESULTS: Genetic causes were identified in 28% of the 110 patients: 7% had inherited metabolic disorders including pyridoxine dependent epilepsy caused by ALDH7A1 mutation, Menkes disease, pyridox(am)ine-5-phosphate oxidase deficiency, cobalamin G deficiency, methylenetetrahydrofolate reductase deficiency, glucose transporter 1 deficiency, glycine encephalopathy, and pyruvate dehydrogenase complex deficiency; 21% had other genetic causes including genetic syndromes, pathogenic copy number variants on array comparative genomic hybridization, and epileptic encephalopathy related to mutations in the SCN1A, SCN2A, SCN8A, KCNQ2, STXBP1, PCDH19, and SLC9A6 genes. Forty-five percent of patients obtained a genetic diagnosis by targeted next-generation sequencing epileptic encephalopathy panels. It is notable that 4.5% of patients had a treatable inherited metabolic disease. SIGNIFICANCE: To the best of our knowledge, this is the first study to combine inherited metabolic disorders and other genetic causes of epileptic encephalopathy. Targeted next-generation sequencing panels increased the genetic diagnostic yield from <10% to >25% in patients with epileptic encephalopathy.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".