P.017 Worldwide neurologist survey on management of autoimmune encephalitis
Bibliographic record
Abstract
Background: Diagnosis of autoimmune encephalitis (AE) is complicated by issues with sensitivity/specificity of antibody testing, non-specific MRI/EEG/CSF findings, and competing differential diagnoses. We explored practice differences in AE diagnosis and management. Methods: We utilized a worldwide electronic survey with practice-related demographic questions, and clinical questions about 2 cases: (1) a 20-year-old woman with a neuropsychiatric presentation strongly suspicious of AE, (2) a 40-year-old man with new temporal lobe seizures and cognitive impairment. Responses among different groups were compared using multi-variable logistic regression. Results: We received 1,333 responses from 94 countries; 12.0% identified as neuro-immunologists. Case 1: Those treating >5 AE cases/year were more likely to send antibodies in both serum and CSF (aOR vs 0/year: 3.29,95%CI 1.31-8.28,p=0.011), pursue empiric immunotherapy (aOR:2.42,1.33-4.40,p=0.004), and continue immunotherapy despite no response and negative antibodies at 2-weeks (aOR:1.65,1.02-2.69,p=0.043). Case 2: Neuro-immunologists were more likely to send antibodies in both serum and CSF (aOR:1.80,1.12-2.90,p=0.015). Those seeing >5 AE cases/year (aOR:1.86,1.22-2.86,p=0.004) were more likely to start immunotherapy without waiting for antibody results. Conclusions: Our findings highlight the heterogenous management of AE. Neuroimmunologists and those treating more AE cases generally take a more proactive approach to testing and immunotherapy than peers. Results emphasize the need for higher-quality treatment/outcome data and evidence-based guidelines.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".