The spectrum of SCN1A-related infantile epileptic encephalopathies
Bibliographic record
Abstract
The relationship between severe myoclonic epilepsy of infancy (SMEI or Dravet syndrome) and the related syndrome SMEI-borderland (SMEB) with mutations in the sodium channel alpha 1 subunit gene SCN1A is well established. To explore the phenotypic variability associated with SCN1A mutations, 188 patients with a range of epileptic encephalopathies were examined for SCN1A sequence variations by denaturing high performance liquid chromatography and sequencing. All patients had seizure onset within the first 2 years of life. A higher proportion of mutations were identified in patients with SMEI (52/66; 79%) compared to patients with SMEB (25/36; 69%). By studying a broader spectrum of infantile epileptic encephalopathies, we identified mutations in other syndromes including cryptogenic generalized epilepsy (24%) and cryptogenic focal epilepsy (22%). Within the latter group, a distinctive subgroup designated as severe infantile multifocal epilepsy had SCN1A mutations in three of five cases. This phenotype is characterized by early onset multifocal seizures and later cognitive decline. Knowledge of an expanded spectrum of epileptic encephalopathies associated with SCN1A mutations allows earlier diagnostic confirmation for children with these devastating disorders.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".