EEG features of absence seizures in idiopathic generalized epilepsy: Impact of syndrome, age, and state
Bibliographic record
Abstract
PURPOSE: Factors influencing the electroencephalography (EEG) features of absence seizures in newly presenting children with idiopathic generalized epilepsy (IGE) have not been rigorously studied. We examined how specific factors such as state, provocation, age, and epilepsy syndrome affect the EEG features of absence seizures. METHODS: Children with untreated absence seizures were studied using video-EEG recording. The influence of state of arousal, provocation (hyperventilation, photic stimulation), age, and epilepsy syndrome on specific EEG features was analyzed. RESULTS: Five hundred nine seizures were evaluated in 70 children with the following syndromes: childhood absence epilepsy (CAE) 37, CAE+ photoparoxysmal response (PPR) 10, juvenile absence epilepsy (JAE) 8, juvenile myoclonic epilepsy (JME) 6, and unclassified 9. Polyspikes occurred in all syndromes but were more common in JME. They were brought out by drowsiness and sleep in fragments of generalized spike and wave (GSW). Polyspikes were more likely to occur during photic stimulation, but were not influenced by age independently. GSW was more likely to be disorganized in JME than JAE, and in JAE than CAE. Increasing age and levels of arousal were more likely to result in organized GSW. Factors specific to each child independently influenced EEG features; the nature of these factors has not been identified. DISCUSSION: The EEG features of absence seizures are influenced by a complex interaction of age, epilepsy syndrome, level of arousal, provoking factors, and other intrinsic factors. Epilepsy syndrome alone cannot predict specific features of GSW; however, JME is more frequently associated with polyspikes and disorganization of the paroxysm.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".