De novo <i><scp>SCN</scp>1A</i> pathogenic variants in the <scp>GEFS</scp>+ spectrum: Not always a familial syndrome
Bibliographic record
Abstract
Genetic epilepsy with febrile seizures plus (GEFS+) is a familial epilepsy syndrome characterized by heterogeneous phenotypes ranging from mild disorders such as febrile seizures to epileptic encephalopathies (EEs) such as Dravet syndrome (DS). Although DS often occurs with de novo SCN1A pathogenic variants, milder GEFS+ spectrum phenotypes are associated with inherited pathogenic variants. We identified seven cases with non-EE GEFS+ phenotypes and de novo SCN1A pathogenic variants, including a monozygotic twin pair. Febrile seizures plus (FS+) occurred in six patients, five of whom had additional seizure types. The remaining case had childhood-onset temporal lobe epilepsy without known febrile seizures. Although early development was normal in all individuals, three later had learning difficulties, and the twin girls had language impairment and working memory deficits. All cases had SCN1A missense pathogenic variants that were not found in either parent. One pathogenic variant had been reported previously in a case of DS, and the remainder were novel. Our finding of de novo pathogenic variants in mild phenotypes within the GEFS+ spectrum shows that mild GEFS+ is not always inherited. SCN1A screening should be considered in patients with GEFS+ phenotypes because identification of pathogenic variants will influence antiepileptic therapy, and prognostic and genetic counseling.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".