Long-Term Cognitive Outcomes in Patients with Autoimmune Encephalitis
Bibliographic record
Abstract
BACKGROUND: A need exists to characterise the long-term cognitive outcomes in patients who recovered from autoimmune encephalitis and to identify the modifiable factors associated with improved outcomes. METHODS: We retrospectively analysed data from patients diagnosed with autoimmune encephalitis in our outpatient autoimmune encephalitis clinic over a 5-year period, where the Montreal Cognitive Assessment (MoCA) is routinely administered. RESULTS: In total, 21 patients met the inclusion criteria, of whom 52% had persistent cognitive impairment at their latest follow-up (median delay to testing=20 months, range 13-182). Visuospatial and executive abilities, language, attention, and delayed recall were predominantly affected. Patients with status epilepticus at presentation had lower total MoCA scores at their last follow-up (median total score 21, range 15-29) compared with patients without status epilepticus at presentation (median total score 27.5, range 21-30; r 2=0.366, p=0.004). Patients who experienced delays of more than 60 days from symptom onset to initiation of treatment (either immunosuppression or tumour removal) were more likely to have a MoCA score compatible with cognitive impairment at their last follow-up (r 2=0.253, p=0.0239; z-score=-2.01, p=0.044). CONCLUSIONS: Our study suggests that the MoCA may be used to evaluate cognition in recovering patients with autoimmune encephalitis. Delays to treatment shorter than 60 days and absence of status epilepticus at onset were associated with better performance on the MoCA obtained more than 1 year after symptom onset, and may predict better long-term 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 | 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".