P.027 Autoimmune encephalitis: modifiable and non-modifiable predictors of relapse
Bibliographic record
Abstract
Background: Approximately 25% of encephalitis cases in North America are autoimmune (AIE). For most forms of AIE, it is unclear which patients have the highest relapse risk and whether standard treatments reduce this risk. Our objective was to determine the overall risk of relapse and whether chronic immunosuppressive therapy modifies that risk. Methods: We performed a chart review consisting of all 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 patients, 17/39 (44%) patients relapsed, and most relapses (76%) occurred within 3 years. Patients not on any immunosuppression at the time of relapse had a greater increase in CASE score, a proxy for presentation severity, at relapse compared to those on immunosuppression (p=0.0035). Conclusions: The risk of relapse in AIE is high (44%). Immunosuppression at the time of relapse, which may occur up to 3 years after initial presentation, lessens the relapse severity, although it remains unclear if it can reliably prevent relapses. Our data enforces the importance of long-term follow up and that ongoing immunosuppression may be helpful, particularly in the first 3 years after initial presentation.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".