Anti-NMDA Receptor Encephalitis in a Patient with Rheumatoid Arthritis
Bibliographic record
Abstract
To the Editor: Anti-NMDA receptor (NMDAR) encephalitis is a form of autoimmune encephalitis that was first described in young women with ovarian teratoma1. We report the case of a 61-year-old woman with seropositive rheumatoid arthritis (RA) who developed a non-paraneoplastic encephalitis related to antibodies against NMDAR in serum and cerebrospinal fluid (CSF). Her medical history included dyslipidemia and RA. For the latter, she had received various immunosuppressive agents (sulfasalazine, leflunomide, and etanercept) and was at presentation under treatment with methotrexate (MTX) and rituximab (RTX). She presented with dizziness, unsteady gait, and proximal muscle weakness. No seizures, dyskinesias, autonomic dysfunction, or behavioral changes were present. Outpatient examination was performed, including head and cervical magnetic resonance imaging, whole-body computed tomography (CT), cerebral single photon emission–CT scan, and neurophysiological studies; nothing relevant was found. Nevertheless, RTX was preventively suspended and treatment with MTX in monotherapy was continued. Other concurrent medications were suspended … Address correspondence to Dr. L. Ruiz, ESIR Service, Hospital Príncipe de Asturias, Ctra Alcalá-Meco s/n, 28805 Alcalá de Henares, Spain. E-mail: lucia.ruiz{at}salud.madrid.org
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.010 | 0.006 |
| 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".