Long-Term Seizure Outcomes in Autoimmune Encephalitis
Bibliographic record
Abstract
Introduction: Seizures are common in autoimmune encephalitis (AE), but identifying patients at risk of chronic epilepsy in the post-acute phase remains challenging. This study aims to identify risk factors of treatment-resistant postencephalitic epilepsy. Methods: This retrospective cohort study included patients with AE who experienced new-onset seizures within one year of symptom onset from two tertiary care centers in New York. EEG findings were analyzed separately based on whether the EEG recording was obtained in the acute (<3 months from symptom onset) or subacute phase. A multivariate logistic regression model was used to identify independent predictors of postencephalitic epilepsy. Results: Eighty-nine patients were included (median age: 33 years). Neural antibodies were present in 73% of patients (NMDAR: 35, LGI1: 19, GAD65: 9, Hu: 1, AGNA-1: 1). Over a median follow-up of 4.9 years, 29.2% developed treatment-resistant postencephalitic epilepsy. Independent predictors of postencephalitic epilepsy included focal slowing on acute EEG (OR 0.15, CI 0.02-0.90), interictal epileptiform discharges (IEDs) or periodic discharges (PDs) on subacute EEG (OR 20.01, CI 1.94-206.44), and cell surface antibodies (OR 0.21, CI 0.05-0.89). Immunotherapy within three months of onset was associated with decreased epilepsy development in patients with neural antibodies (OR 4.16, CI 1.11-16.30). Conclusions: Nearly one-third of patients with AE and acute seizures developed treatment-resistant postencephalitic epilepsy, with significant predictors including absence of focal slowing on acute EEG, presence of IEDs and PDs on subacute EEG, absence of cell surface antibodies, and absence of early immunotherapy treatment of patients with positive neural antibodies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".