Familial neonatal seizures in 36 families: Clinical and genetic features correlate with outcome
Bibliographic record
Abstract
OBJECTIVE: We evaluated seizure outcome in a large cohort of familial neonatal seizures (FNS), and examined phenotypic overlap with different molecular lesions. METHODS: Detailed clinical data were collected from 36 families comprising two or more individuals with neonatal seizures. The seizure course and occurrence of seizures later in life were analyzed. Families were screened for KCNQ2, KCNQ3, SCN2A, and PRRT2 mutations, and linkage studies were performed in mutation-negative families to exclude known loci. RESULTS: Thirty-three families fulfilled clinical criteria for benign familial neonatal epilepsy (BFNE); 27 of these families had KCNQ2 mutations, one had a KCNQ3 mutation, and two had SCN2A mutations. Seizures persisting after age 6 months were reported in 31% of individuals with KCNQ2 mutations; later seizures were associated with frequent neonatal seizures. Linkage mapping in two mutation-negative BFNE families excluded linkage to KCNQ2, KCNQ3, and SCN2A, but linkage to KCNQ2 could not be excluded in the third mutation-negative BFNE family. The three remaining families did not fulfill criteria of BFNE due to developmental delay or intellectual disability; a molecular lesion was identified in two; the other family remains unsolved. SIGNIFICANCE: Most families in our cohort of familial neonatal seizures fulfill criteria for BFNE; the molecular cause was identified in 91%. Most had KCNQ2 mutations, but two families had SCN2A mutations, which are normally associated with a mixed picture of neonatal and infantile onset seizures. Seizures later in life are more common in BFNE than previously reported and are associated with a greater number of seizures in the neonatal period. Linkage studies in two families excluded known loci, suggesting a further gene is involved in BFNE.
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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 | 0.003 |
| 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".