P.080 Effects of REM sleep in anti-NMDA receptor encephalitis with extreme delta brush pattern
Bibliographic record
Abstract
Background: Seizures are rare in REM sleep. To our knowledge, the effects of different sleep stages in anti-NMDA encephalitis have not been studied. Methods: Case report. Results: 32 year-old healthy female presented with acute fluctuating level of consciousness with episodic impulsivity, disorientation, and emotional outbursts following 3 days of headache. Her temperature was 37.8°C and she was started on ceftriaxone, vancomycin, and acyclovir. CSF only showed a total nuclear cell count of 182 and pleocytosis. MRI revealed increased T2 hyperintense right lateral temporal and parietal cortical thickening. EEG revealed right frontotemporal seizures with left sided rhythmic jerking, and an extreme delta brush pattern. Interestingly, seizures and extreme delta brush disappeared in REM sleep. After HSV PCR was negative, she was immunosuppressed with corticosteroids, IVIG, rituximab, and cyclophosphamide, given the extreme delta brush pattern. Repeat CSF analysis eventually yielded highly positive anti-NMDA antibodies. Ten weeks later, she became seizure free. Conclusions: For the first time, we report REM sleep in a case of anti-NMDA encephalitis during which there was absence of extreme delta brush and ictal EEG patterns. This observation may provide insight into the ongoing debate over whether extreme delta brush is an ictal EEG pattern.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".