P.026 Autoimmune encephalitis: modifiable and non-modifiable predictors of relapse
Bibliographic record
Abstract
Background: Approximately 25% of encephalitis cases in North America are immune mediated. For most forms of autoimmune encephalitis (AIE), risk of relapse is unclear and little evidence exists to guide which patients have the highest risk and whether standard treatments reduce this risk. Our objective was to determine the factors associated with AIE relapse. Methods: We performed a chart review consisting of patients with AIE presenting to the Calgary Neuro-Immunology Clinic and Tom Baker Cancer Centre between 2015 and 2020. Predictors of relapse were determined with use of t-test. Results: Outcome data was assessable in 39/40 patients, 17/39 (44%) patients relapsed. Seropositive patients and those with abnormal CSF were more likely to relapse, although neither reached statistical significance (p=0.12, 0.059). Patients with longer duration of steroid and steroid sparing treatment prior to relapse, and those on steroids at the time of relapse, had milder relapses (p=0.024, 0.026, 0.047). There was no difference in steroid or steroid sparing treatment use at 3, 6, and 12 months between groups. Conclusions: Risk of relapse in AIE is high (44%), with most relapses occurring in the first 3 years. Continuous immunosuppression lessens the severity of relapse, although our study did not confirm it reduced the occurrence of relapse.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.001 |
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".