D.05 SREDA-like temporal EEG seizure pattern in LGI1-antibody mediated encephalitis
Bibliographic record
Abstract
Background: Leucine-rich glioma inactived-1 (LGI1) antibodies are associated with limbic encephalitis and distinctive seizure types, which are typically immunotherapy-responsive. While nonspecific EEG abnormalities are commonly seen, specific EEG characteristics are not currently understood to be useful for suspecting the clinical diagnosis. Based on initial observations in two patients, we analyzed the EEG recordings in a larger series of patients and describe a novel ictal pattern that can suggest the diagnosis of LGI1-antibody mediated encephalitis, even in the absence of common clinical features. Methods: Clinical and EEG data were collected in nine patients with LGI1 antibodies. Results: Psychiatric and cognitive symptoms were common, as were tonic seizures associated with EEG electrodecremental events (often with the so-called faciobrachial dystonic semiology). A rarity or absence of interictal epileptiform discharges contrasted with frequent subclinical temporal lobe seizures in some patients, which at times showed characteristics similar to subclinical rhythmic electrographic discharges of adults (SREDA), including sensitivity to hyperventilation. Conclusions: LGI1-antibody mediated encephalitis may be associated with tonic seizures and corresponding electrodecremental events, as well as an unusual SREDA-like pattern of frequent subclinical temporal lobe seizures, which may be triggered by hyperventilation, all in the setting of rare interictal epileptiform discharges.
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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) | 0.006 | 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".