Beyond Myoclonus-Seizures, Epilepsy Syndromes and Atypical Electroencephalographic (EEG) Findings in Children With Subacute Sclerosing Panencephalitis
Bibliographic record
Abstract
BackgroundSubacute sclerosing panencephalitis is typically characterized by myoclonic jerks, cognitive decline, movement disorders, and periodic complexes on electroencephalography (EEG). Although myoclonus is a hallmark feature, other seizure types including generalized/focal seizures are less commonly described in subacute sclerosing panencephalitis. We aimed to study seizure frequency, types, spectrum of epilepsy syndromes, and atypical EEG findings among children with subacute sclerosing panencephalitis.Materials and MethodsA retrospective chart review of 100 children (aged 1-18 years) diagnosed with subacute sclerosing panencephalitis (April 2020-April 2024) was conducted. Data collected included demographics, clinical features, seizure semiology, EEG, and magnetic resonance imaging (MRI) findings. Outcome measures included the proportion of children experiencing seizures beyond myoclonus, the spectrum of seizures and epilepsy syndromes as per the International League Against Epilepsy (ILAE) 2017 seizure classification and the ILAE 2022 diagnostic framework for electroclinical syndromes, respectively, and description of other atypical EEG patterns.ResultsAmong 100 children (73% males, age range 5.5-10 years), 54% had seizures beyond myoclonus, which included bilateral tonic-clonic seizures in 48 children, focal seizures in 5 children, and 1 child with epileptic spasms. Six children had classifiable epilepsy syndromes, including 5 children with epileptic encephalopathy with spike-wave activation in sleep and 1 child with infantile epileptic spasms syndrome. Atypical EEG patterns, seen in 22%, included epileptic encephalopathy with spike-wave activation in sleep-like pattern, modified hypsarrhythmia-like pattern, electrodecrement within periodic complexes, etc, which correlated with advanced stages of subacute sclerosing panencephalitis.ConclusionsSubacute sclerosing panencephalitis can often mimic epileptic encephalopathies. Atypical seizure semiologies and varied EEG patterns highlight the need for strong clinical suspicion to avoid misdiagnosis and delayed disease recognition, especially in endemic countries like India.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".