Anti-N-methyl-D-aspartate receptor encephalitis presenting as atypical psychosis in multiple sclerosis: a case report
Bibliographic record
Abstract
BACKGROUND: Anti-N-methyl-D-aspartate receptor (anti-NMDAR) encephalitis is an autoimmune disorder which often presents with neuropsychiatric symptoms. A large proportion of cases are associated with an identifiable tumor, most commonly ovarian teratoma. However, recent literature has also described an overlap of anti-NMDAR encephalitis and demyelinating syndromes. Cases have been reported of anti-NMDAR encephalitis in patients with ADEM, optic neuritis, myelitis and multiple sclerosis. This link is considered rare, however has important clinical implications as treatments and prognosis may differ. CASE PRESENTATION: A 33-year-old female with a history of multiple sclerosis presented with new-onset neuropsychiatric symptoms. After substance-induced psychosis was ruled out, she was admitted to the medical ward for work up of psychosis secondary to multiple sclerosis. However, the consultation-liaison psychiatry service noted atypical symptoms which were concerning for autoimmune encephalitis. Admission to a psychiatric inpatient ward was deferred. Anti-NMDAR encephalitis was diagnosed with CSF analysis demonstrating lymphocytic pleocytosis and anti-NMDAR antibodies. In addition to first-line treatment of encephalitis with steroids, second-line immunotherapies were also implemented given the patient's underlining demyelinating syndrome. The patient's neurologic and psychiatric symptoms began to improve. CONCLUSIONS: There is literature to demonstrate a possible connection between anti-NMDAR encephalitis and demyelinating syndromes. As such, autoimmune encephalitis should be considered in patients with multiple sclerosis presenting with atypical symptoms. Determining the correct diagnosis is crucial to inform the appropriate treatment protocol, and to improve prognosis.
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.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.008 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".