Clinical features of sleep‐related hypermotor epilepsy in relation to the seizure‐onset zone: A review of 135 surgically treated cases
Bibliographic record
Abstract
OBJECTIVES: Sleep-related hypermotor epilepsy (SHE), formerly nocturnal frontal lobe epilepsy, is characterized by abrupt and typically sleep-related seizures with motor patterns of variable complexity and duration. They seizures arise more frequently in the frontal lobe than in the extrafrontal regions but identifying the seizure onset-zone (SOZ) may be challenging. In this study, we aimed to describe the clinical features of both frontal and extrafrontal SHE, focusing on ictal semiologic patterns in order to increase diagnostic accuracy. METHODS: We retrospectively analyzed the clinical features of patients with drug-resistant SHE seen in our center for epilepsy surgery. Patients were divided into frontal and extrafrontal SHE (temporal, operculoinsular, and posterior SHE). We classified seizure semiology according to four semiology patterns (SPs): elementary motor signs (SP1), unnatural hypermotor movements (SP2), integrated hypermotor movements (SP3), and gestural behaviors with high emotional content (SP4). Early nonmotor manifestations were also assessed. RESULTS: Our case series consisted of 91 frontal SHE and 44 extrafrontal SHE cases. Frontal and extrafrontal SHE shared many features such as young age at onset, high seizure-frequency rate, high rate of scalp electroencephalography (EEG) and magnetic resonance imaging (MRI) abnormalities, similar histopathologic substrates, and good postsurgical outcome. Within the frontal lobe, SPs were organized in a posteroanterior gradient (SP1-4) with respect to the SOZ. In temporal SHE, SP1 was rare and SP3-4 frequent, whereas in operculoinsular and posterior SHE, SP4 was absent. Nonmotor manifestations were frequent (70%) and some could provide valuable localizing information. SIGNIFICANCE: Our study shows that the presence of certain SP and nonmotor manifestations may provide helpful information to localize seizure onset in patients with SHE.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".