Automatisms in Absence Seizures in Children With Idiopathic Generalized Epilepsy
Bibliographic record
Abstract
BACKGROUND: Automatisms are well recognized to occur in complex partial seizures; however, their occurrence in generalized epilepsies is not always appreciated. There has been considerable debate regarding the nature, triggers, and timing of automatisms in absence seizures. OBJECTIVES: To examine the frequency and nature of automatisms in new-onset absence seizures and assess the influence of the state of arousal, provocation, age, and epilepsy syndrome on the presence and type of automatisms. DESIGN: Analysis of absence seizures through video electroencephalogram (EEG) recordings. SETTING: British Columbia's Children's Hospital, Vancouver, British Columbia, Canada. PATIENTS: Seventy consecutive children with new-onset untreated absence seizures in idiopathic generalized epilepsy recruited between January 1, 1992, and June 30, 1997. MAIN OUTCOME MEASURES: Each seizure was analyzed for the presence and characteristics of automatisms. The influence of the following variables on the presence of automatisms was statistically analyzed: state of arousal (awake, drowsy, asleep), provocation (hyperventilation, photic stimulation), age, and epilepsy syndrome. RESULTS: Automatisms occurred in 163 of 405 seizures (40%) in 53 of 70 children (76%). Automatisms were more likely in longer seizures and hyperventilation. Only 23% of spontaneous awake seizures had automatisms. Automatisms were similar for an individual child; however, automatisms were not present in all their seizures. Age, epilepsy syndrome, or state of alertness had no effect on the presence of automatisms. CONCLUSIONS: Automatisms are frequently seen during childhood absence seizures. The high frequency of automatisms during EEG recordings is predominantly due to the effect of hyperventilation. Their preponderance during longer seizures may relate to opportunity for automatisms to occur. The characteristic pattern of automatisms suggests a reactive phenomenon to internal and external stimuli.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".