Post‐ictal rage and aggression: a video‐EEG study
Bibliographic record
Abstract
Post-ictal rage and aggression have been mentioned in the literature but have rarely been documented by video-EEG recording. We studied a patient with dramatic, episodic, seizure-related rage and violence. This mentally retarded man had a lifelong history of seizures. He developed increasing episodic rage and aggression. Caregivers were afraid of him, although there was no record of directed violence. In one of these episodes he fractured his tibia and fibula. Interictal discharges arose from both temporal areas independently. He had focal seizures with secondary generalization. Immediately after cessation of the ictal discharge he became greatly agitated, with undirected aggression, loud screaming, kicking and fighting against the restraints. A video sequence illustrates the behavior. Imaging studies showed bilateral, periventricular, nodular heterotopia in the lateral aspect of both temporal horns and the trigones. Increasingly frequent, severe bursts of rage and aggression were proven to be post-ictal. Documented attacks occurred while he was restrained and this may have been a factor in their severity. Such attacks however, have been described while he was not restrained and these increased in severity and frequency over time. Developmental abnormalities with periventricular, nodular heterotopia in the region of the trigones and inferomesial temporal areas are considered to be causally related to his retardation and epilepsy. [Published with video sequences].
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".