Sleep-related epileptic behaviors and non-REM-related parasomnias: Insights from stereo-EEG
Bibliographic record
Abstract
During the last decade, many clinical and pathophysiological aspects of sleep-related epileptic and non-epileptic paroxysmal behaviors have been clarified. Advances have been achieved in part through the use of intracerebral recording methods such as stereo-electroencephalography (S-EEG), which has allowed a unique "in vivo" neurophysiological insight into focal epilepsy. Using S-EEG, the local features of physiological and pathological EEG activity in different cortical and subcortical structures have been better defined during the entire sleep-wake spectrum. For example, S-EEG has contributed to clarify the semiology of sleep-related seizures as well as highlight the specific epileptogenic networks involved during ictal activity. Moreover, intracerebral EEG recordings derived from patients with epilepsy have been valuable to study sleep physiology and specific sleep disorders. The occasional co-occurrence of NREM-related parasomnias in epileptic patients undergoing S-EEG investigation has permitted the recordings of such events, highlighting the presence of local electrophysiological dissociated states and clarifying the underlying pathophysiological substrate of such NREM sleep disorders. Based on these recent advances, the authors review and summarize the current and relevant S-EEG literature on sleep-related hypermotor epilepsies and NREM-related parasomnias. Finally, novel data and future research hypothesis will be discussed.
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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 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".