Long-Term Follow-Up of Non-Paraneoplastic Autoimmune Encephalitis: A Canadian Single-Center Experience
Bibliographic record
Abstract
ABSTRACT: Background: Autoimmune encephalidities (AIE) are becoming an increasingly recognized cause of encephalitis. While diagnosis and acute management are well described, information on long-term management and outcomes is limited. Given this, we reviewed 5 years of AIE patients, reporting on chronic management, relapse incidence and possible relapse predictors. Methods: We performed a chart review of all patients with non-paraneoplastic AIE presenting to Calgary Neuro-Immunology Clinic and Tom Baker Cancer Centre between 2015 and 2020. Severity of relapse was determined using the Clinical Assessment Scale in Autoimmune Encephalitis (CASE). Variables were assessed with descriptive analysis and/or t -test. Results: Patients were followed for a mean of 38.2 months. Outcome data were assessable in 37/38 patients. Relapse rate ranged from 0% (GFAP) to 67% (NMDA), with a mean of 46%. Most relapses (76%) occurred within 3 years. Time to treatment initiation at relapse was significantly shorter than initial presentation ( p = 0.0015), and patients had less severe relapses compared to initial presentation (CASE score 5.18 vs 6.53; p = 0.040). Use of chronic immunotherapy did not appear to impact overall relapse risk, although patients on any immunotherapy at relapse had milder relapses based on ΔCASE ( p = 0.0035). Conclusion: Relapse was not uncommon (46%) for various AIE subtypes in our cohort, particularly within the first 3 years. Our data enforce the importance of long-term follow-up, which in our study allowed for earlier treatment and less severe relapses compared to initial presentation, as well as the need to further explore which patients would benefit from chronic immunotherapy.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".