Autoimmune Encephalitis and Autoantibodies: A Review of Clinical Implications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it