Genetic generalized and focal epilepsy prevalence in the North American SUDEP Registry
Bibliographic record
Abstract
OBJECTIVE: To assess relative rates and clinical features of patients with genetic generalized epilepsy (GGE), focal epilepsy (FE), and developmental encephalopathic epilepsy (DEE) in the North American SUDEP Registry (NASR). METHODS: We identified all adjudicated definite, definite plus, and probable sudden unexpected death in epilepsy (SUDEP) cases (n = 262) and determined epilepsy type (GGE, FE, or DEE) from medical record review including history, imaging and EEG results, genetics, and next-of-kin interviews. RESULTS: Of the 262 SUDEP cases, 41 occurred in GGE, 95 in FE, 24 in DEE, and 102 were unclassifiable. GGE cases comprised 26% of NASR cases with an epilepsy syndrome diagnosis. The relative frequency of FE:GGE was slightly lower (2.3:1) than in population cohorts (2.1-6:1). Compared to patients with FE, patients with GGE had similar (1) ages at death and epilepsy onset and rates of (2) terminal and historical antiseizure medication adherence; (3) abnormal cardiac pathology; (4) illicit drug/alcohol use histories; and (5) sleep state when SUDEP occurred. CONCLUSIONS: GGE cases were relatively overrepresented in NASR. Because GGEs are less often treatment-resistant than FE or DEE, seizure type rather than frequency may be critical. Many people with GGE predominantly have generalized tonic-clonic seizures (GTCS) when they have uncontrolled or breakthrough seizures, whereas patients with FE more commonly experience milder seizures. Future mechanistic SUDEP studies should assess primary and focal-to-bilateral GTCS to identify potential differences in postictal autonomic and arousal disorders and to determine the differential role that lifestyle factors have on breakthrough seizures and seizure types in GGE vs FE to effectively target SUDEP mechanisms and prevention.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".