Anti–<i>N</i>‐methyl‐<scp>D</scp>‐aspartate receptor encephalitis: A newly recognized inflammatory brain disease in children
Bibliographic record
Abstract
OBJECTIVE: Anti-N-methyl-D-aspartate receptor (anti-NMDAR) encephalitis is a newly recognized antineuronal antibody-mediated inflammatory brain disease that causes severe psychiatric and neurologic deficits in previously healthy children. The present study was undertaken to describe characteristic clinical features and outcomes in children diagnosed as having anti-NMDAR encephalitis. METHODS: Consecutive children presenting over a 12-month period with newly acquired psychiatric and/or neurologic deficits consistent with anti-NMDAR encephalitis and evidence of central nervous system (CNS) inflammation were screened. Children were included in the study if they had confirmatory evidence of anti-NMDAR antibodies in the serum and/or cerebrospinal fluid. Features at clinical presentation and results of investigations were recorded. Type and duration of treatment and outcomes at last followup were documented. RESULTS: Seven children were screened, and 3 children with anti-NMDAR encephalitis were identified. All patients presented with neurologic and/or psychiatric abnormalities, seizures, speech disorder, sleep disturbance, and fluctuating level of consciousness. The 2 older patients had more prominent psychiatric features, while the younger child had significant autonomic instability and prominent involuntary movement disorder. None had an underlying tumor. Immunosuppressive therapy resulted in near or complete recovery; however, 2 of the patients had early relapse necessitating re-treatment. CONCLUSION: Anti-NMDAR encephalitis is an important cause of neuropsychiatric deficits in children, which must be included in the differential diagnosis of CNS vasculitis and other inflammatory brain diseases. Early diagnosis and treatment are essential for neurologic recovery.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".