Autoimmune Encephalitis and Autoantibodies: A Review of Clinical Implications
Bibliographic record
Abstract
BACKGROUND: Autoimmune encephalitis (AE) is a common cause of encephalitis. We review the most recent evidence on this neuroimmune condition and autoantibody testing currently available. CONTENT: Clinical criteria, neuroimaging and electroencephalography can facilitate the diagnosis of AE prior to obtaining autoantibody testing results, and lead to a diagnosis of AE even in the absence of a recognized antibody. Early treatment of AE has been found to correlate with improved long-term functional and cognitive outcomes. We suggest a clinical approach to diagnosis based on the predominant area of nervous system involvement and the results of ancillary testing that are widely available. We also propose a 2-tiered approach to the acute management of probable or definite AE. We, finally, provide guidance on the long-term management of AE-a challenging and understudied area. SUMMARY: Much work remains to be done to improve the care of patients with AE. As understanding of the pathophysiology and predisposing factors underlying this condition steadily increases, a more evidence-based, targeted approach to the treatment of AE is still desired. Nonetheless, looking at the progress made over the past 2 decades, since the discovery of the first autoantibodies associated with AE, one cannot help but feel optimistic about the road ahead.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".