Gamma-Aminobutyric Acid Type A Receptor Encephalitis Primarily Manifesting as Cognitive Dysfunction and Subclinical Epilepsy: A Case Report and Literature Review
Bibliographic record
Abstract
Abstract Background: Anti-γ-aminobutyric acid type A receptor (anti-GABAA R) encephalitis is a neurological disorder that primarily manifests as cognitive dysfunction and seizures. Affected patients rarely present with subclinical epilepsy; thus, they are prone to misdiagnosis and underdiagnosis due to a lack of available tests during early disease stages. Case presentation: An 83-year-old male presented with a 20-day history of progressively worsening hypomnesis. On admission, cognitive dysfunction was indicated based on a Simple Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) scores of 8 and 4, respectively. Electroencephalography (EEG) showed paroxysmal spike-slow complex wave bursts in all regions, and cerebrospinal fluid was positive for anti-GABAA R β3 antibodies (titer 1:3.2). The patient was diagnosed with anti-GABAA acid A encephalitis and treated with methylprednisolone sodium succinate, gamma globulin, and mycophenolate mofetil capsules. After treatment, hypomnesis gradually improved, and EEG findings transitioned from paroxysmal spike-slow complex wave bursts in various regions to clusters of predominantly theta and delta waves. Thereafter, the patient was discharged from the hospital. After discharge, the patient continued taking oral methylprednisolone and mycophenolate mofetil capsules, but self-discontinued methylprednisolone after one month. At the five-month follow-up, the hypomnesis was significantly improved, and MMSE and MoCA scores were 18 and 14, respectively, revealing moderate cognitive impairment. Conclusions: Anti-GABAA R encephalitis manifesting clinically as cognitive dysfunction and subclinical epilepsy is rare. Therefore, early, comprehensive, and meticulous ancillary examinations with timely and effective treatment planning are crucial for improving the duration of recovery and prognosis of the disease.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".