Severe Relapsing Autoimmune Encephalitis with GABA<sub>A</sub> Receptor, Titin, and AchR Antibodies in a Patient with Thymoma: A Case Report
Bibliographic record
Abstract
Introduction: We report a challenging case of autoimmune encephalitis in a patient with a thymoma harboring titin and acetylcholine receptor antibodies, who experienced multiple relapses despite thymectomy and aggressive first-line immunotherapy, and for whom GABAA receptor antibodies were ultimately identified. Case Presentation: This 40-year-old man presented with headaches, weakness, diplopia, hearing loss, and seizures progressing to status epilepticus. Brain MRI showed multifocal cortical and subcortical T2/fluid attenuated inversion recovery hyperintense lesions without enhancement. Initial neural antibody testing identified only acetylcholine receptor and titin antibodies. He presented multiple severe relapses despite complete thymoma resection, intravenous methylprednisolone with immunoglobulins or plasmapheresis, and mycophenolate mofetil. Second-line immunotherapy with rituximab was successful to alleviate symptoms and normalize the EEG and MRI after identification of anti-GABAA receptor antibodies on more comprehensive neural antibody testing for autoimmune encephalitis. Conclusion: This case demonstrates the complexity and importance of identifying pathogenic antibodies and selecting 2nd line treatment accordingly in patients with autoimmune encephalitis when multiple antibodies coexist. Despite tumor resection, aggressive immunotherapy may be needed to prevent further deterioration in anti-GABAA receptor encephalitis.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| 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".