Autoimmune Encephalitis: Insights Into Immune-Mediated Central Nervous System Injury
Bibliographic record
Abstract
Autoimmune encephalitis (AE) is a category of immune-mediated disorders of the central nervous system (CNS) affecting children and adults. It is characterized by the subacute onset of altered mentation, neurocognitive issues, refractory seizures/drug-resistant epilepsy, movement disorders, and/or autonomic dysfunction. AE is mediated by autoantibodies targeting specific surface components or intracytoplasmic antigens in the CNS, leading to functional or structural alterations. Multiple triggers that induce autoimmunity have been described, which are mainly parainfectious and paraneoplastic. The imaging features of AE often overlap with each other and with other common causes of encephalitis/encephalopathy (infections and toxic-metabolic etiologies). Limbic encephalitis is the most common imaging finding shared by most of these entities. Cortical, basal ganglia, diencephalon, and brainstem involvement may also be present. Cerebellar involvement is rare and is often a part of paraneoplastic degeneration. Owing to an improved understanding of AE, their incidence and detection have increased. Hence, in an appropriate setting, a high degree of suspicion is crucial when reporting clinical MRIs to ensure prompt treatment and better patient outcomes. In this review, we discuss the pathophysiology of AE and common etiologies encountered in clinical practice.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".