Biallelic SCN1A variants with divergent epilepsy phenotypes
Bibliographic record
Abstract
PURPOSE: Pathogenic SCN1A variants most commonly cause autosomal dominant Dravet syndrome and genetic epilepsy with febrile seizures plus (GEFS+). However, rare homozygous SCN1A variants have also been reported. We report two new cases of homozygous SCN1A variants associated with divergent epilepsy phenotypes. METHODS: We retrospectively reviewed the charts of two unrelated patients with different homozygous SCN1A variants. We also reviewed all published cases of biallelic SCN1A pathogenic variants, focusing on the epilepsy phenotypes. RESULTS: Patient 1 had a homozygous c. 1676T>A, (p. Ile559Asn) variant of uncertain significance, inherited from asymptomatic parents. Patient 1 exhibited early afebrile seizures controlled by first-line anti-seizure medications and no febrile seizures or status epilepticus, as well as profound developmental delay, macrocephaly, and mild dysmorphic features. Patient 2 had a homozygous pathogenic c. 4970G>A, (p. Arg1657His) variant carried by asymptomatic parents. This patient presented with early, recurrent, and prolonged febrile seizures, moderate developmental delay, and motor dysfunction and was diagnosed with Dravet syndrome. We identified 16 further cases from the literature. Including our cases, 9/18 (50 %) were diagnosed with Dravet syndrome and 6/18 (33 %) with GEFS+. The mean age of seizure onset was 7 months (range 3-19 months). Phenotypes ranged from intact neurodevelopment with controlled epilepsy to profound developmental delay and refractory epilepsy. CONCLUSION: These cases highlight and expand the phenotypic spectrum associated with biallelic SCN1A variants. While some patients present typically for Dravet/GEFS+, others may present with developmental delay in the absence of febrile seizures or status epilepticus. Further studies are needed to confirm genotype-phenotype relationships.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".