Cognitive Impairment in Autoimmune Encephalitis: Characterization and Predicting Long-term Outcomes
Bibliographic record
Abstract
BACKGROUND: Autoimmune encephalitis (AE) is now emerging as an important cause of reversible cognitive impairment. AIMS: We aimed to study cognitive domains affected in various subtypes of AE, assess the changes in cognitive profile, and factors that predict residual cognitive impairment. METHODS: Cognitive assessment was conducted based on their age at presentation (≤12 years: Vineland Social Maturity Scale, 13-18 years: Montreal Cognitive Assessment and >18 years: Addenbrooke's Cognitive Examination-III). They were screened for psychiatric manifestations by Neuropsychiatric Inventory questionnaire. The assessment was repeated after 6 months. RESULTS: Mean age was 21.8 (21.8 ± 17.0) years. Among 74 (M:F: 29:45) patients, 38 (51%) were less than 18 years of age, 22 (37%) adults and 9 (12%) patients belonged to late onset (more than 45 years). Cognitive impairment was seen in 100% of late onset, 85% adults and 58% children. At follow-up, Cognitive impairment was present in 27 out of 45 (60%) patients. Cognitive impairment was common in patients with late-onset AE and in patients with anti-Leucine-rich glioma inactivated-1 (LGI-1) positivity. Age more than 45 years, mutism, hallucinations, incontinence, altered consciousness, psychiatric manifestations, and modified Rankin Scale (mRS) scores more than three, abnormal EEG were shown to be significantly associated with poor cognitive scores. The cognitive domains affected were attention, fluency, memory as residual deficits. The pattern of cognitive recovery during follow-up showed significant improvement in all domains except for new learning, memory, and fluency. CONCLUSIONS: Cognitive Impairment is common in autoimmune encephalitis with new learning, memory and fluency domains severely affected. Late-onset, abnormal EEG, high mRS scores and coexistent psychiatric disturbances predict poor cognitive outcomes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".