Prolonged Corticosteroids Without Maintenance Immunotherapy for Treatment of Anti-LGI1 Encephalitis
Bibliographic record
Abstract
OBJECTIVES: To analyze outcomes and relapse rate of patients with anti-LGI1 encephalitis referred to the London Health Sciences Centre Autoimmune Neurology Clinic, where prolonged (≥3 months) corticosteroids without steroid-sparing maintenance immunotherapy are the typical treatment approach. METHODS: Retrospective chart review. RESULTS: Eighteen patients with anti-LGI1 encephalitis were identified. The median age at symptom onset was 65 years (interquartile range [IQR]: 62-70 years), and 13 (72%) were men. All patients received corticosteroids, with a median treatment duration of 6.3 months (IQR: 3.8-9.6 months). Other first-line immunotherapies used included IV immunoglobulin (n = 11, 61%) and plasma exchange (n = 2, 11%). Three patients referred for refractory disease received rituximab as second-line immunotherapy. No other steroid-sparing maintenance immunotherapies for anti-LGI1 encephalitis were prescribed. At last follow-up, 16/18 (89%) achieved seizure freedom, and 16/18 (89%) had a favorable modified Rankin Scale score. Among 9 patients who had ≥2 years of follow-up from symptom onset, there was disease relapse in 3 (33%), 2 of whom had been referred for refractory disease. DISCUSSION: Among our patients with anti-LGI1 encephalitis who typically received prolonged corticosteroids without steroid-sparing maintenance immunotherapy, outcomes were generally favorable, and relapses were uncommon outside of refractory disease. Further investigation is needed to clarify the optimal corticosteroid regimen and role of steroid-sparing maintenance immunotherapy in anti-LGI1 encephalitis.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".