Development of LGI1 Antibody Encephalitis after Treatment of Lung Cancer
Bibliographic record
Abstract
Autoimmune synaptic encephalitis is a disorder associated with antibodies targeting the cell surface of neurons or synaptic proteins.Neurological symptoms of autoimmune encephalitis include seizure, memory dysfunction, abnormal behavior, and cognitive impairment.Recently, most patients with idiopathic encephalitis have been identified with autoimmune encephalitis associated with antibodies directed against the extracellular domains of cell surface proteins, which are critical in regulating neuronal excitability.[1][2][3][4] Such proteins include N-methyl-D-aspartate (NMDA), α-amino-3-hydroxy-5methyl-4-isoxazolepropionic acid (AMPA), and γ-aminobutyric acid (GABA)-B receptors, as well as the voltage-gated K + channel complex, consisting of leucine-rich, glioma-inactivated 1 (LGI1) and contactin-associated protein-like 2 (CASPR2).1 LGI1 protein has significant roles in synaptic transmission and myelination; therefore, autoantibodies against LGI1 protein would be a pathogenic cause of autoimmune encephalitis.However, the mechanism underlying LGI1 antibody encephalitis is not fully understood.Also, LGI1-antibody encephalitis has been weakly associated with cancer, in relation to paraneoplastic limbic encephalitis.1,2 Despite this weak association with cancer, the presence of LGI1 antibodies and related clinical features are significant.These conditions presenting LGI1 antibody respond well to immunotherapy and sometimes are associated with hidden malignancy.5 Here we describe an unusual paraneoplastic case of LGI1 antibody encephalitis in a 72-year-old man presenting with symptoms of status epilepticus.Unlike previous cases of LGI1 antibody encephalitis with tumor, our patient's encephalitis developed after surgical removal of and chemotherapy for lung cancer.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".