Clinical spectrum of early onset epileptic encephalopathies caused by <scp><i>KCNQ2</i></scp> mutation
Bibliographic record
Abstract
PURPOSE: KCNQ2 mutations have been found in patients with benign familial neonatal seizures, myokymia, or early onset epileptic encephalopathy (EOEE). In this study, we aimed to delineate the clinical spectrum of EOEE associated with KCNQ2 mutation. METHODS: A total of 239 patients with EOEE, including 51 cases with Ohtahara syndrome and 104 cases with West syndrome, were analyzed by high-resolution melting (HRM) analysis or whole-exome sequencing. Detailed clinical information including electroencephalography (EEG) and brain magnetic resonance imaging (MRI) were collected from patients with KCNQ2 mutation. KEY FINDINGS: A total of nine de novo and one inherited mutations were identified (two mutations occurred recurrently). The initial seizures, which were mainly tonic seizures, occurred in the early neonatal period in all 12 patients. A suppression-burst pattern on EEG was found in most. Only three patients showed hypsarrhythmia on EEG; eight patients became seizure free when treated with carbamazepine, zonisamide, phenytoin, topiramate, or valproic acid. Although the seizures were relatively well controlled, moderate-to-profound intellectual disability was found in all except one patient who died at 3 months. SIGNIFICANCE: De novo KCNQ2 mutations are involved in EOEE, most of which cases were diagnosed as Ohtahara syndrome. These cases showed distinct features with early neonatal onset, tonic seizures, a suppression-burst EEG pattern, infrequent evolution to West syndrome, and good response to sodium channel blockers, but poor developmental prognosis. Genetic testing for KCNQ2 should be considered for patients with EOEE.
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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) | 0.000 | 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".