A.01 Targeted analysis of whole exome sequencing and genotype-phenotype correlation in epileptic encephalopathies
Bibliographic record
Abstract
Background: Epileptic encephalopathy (EE) is a severe condition in which epileptic activity itself may contribute to severe cognitive and behavioural impairments above and beyond what might be expected from the underlying pathology alone. Next generation sequencing technologies such as whole exome sequencing (WES) can detect underlying genetic causes of in EE. Methods: This report describes genotype-phenotype correlation of 29 subjects with unexplained epileptic encephalopathy, in whom WES, targeting a list of 557 epilepsy-associated genes was performed. Epilepsy phenotyping was done according to current ILAE recommendations. Results: Median age at seizure onset was 14 months (range 1-48). Electroclinical syndromes were applicable for 16/29, 8/16 had a definite/likely diagnosis. 6/8 subjects with West syndrome had variants in ALG13, STXBP1, PAFAH1B1, SLC35A2, CDKL5 and ADSL. 2 patients with Dravet syndrome had variants in SCN1A and PCDH19 respectively. 4/29 had unspecified EE and definite/likely diagnosis due to STXBP1, POLG, and KCNQ2 (2) variants. 4/29 had a possible diagnosis involving GABRB3, ARHGEF9, PCDH19 and SCN3A variants. Conclusions: The high diagnostic yield (definite/likely diagnosis in 11/29 = 38%), involving a broad variety of epilepsy-associated genes in different electroclinical syndromes justifies the diagnostic approach of early onset EE by next generation sequencing.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".