Does <i>MAP2</i> have a Role in Predicting the Development of Anti-NMDAR Encephalitis Associated with Benign Ovarian Teratoma? A Report of Six New Pediatric Cases
Bibliographic record
Abstract
Anti-N-methyl-d-aspartate receptor (anti-NMDAR) encephalitis is a potentially fatal neurologic syndrome in which patients present with a spectrum of central nervous system deficits. Sixty percent of the cases can be attributed to the presence of tumors, most often ovarian teratomas. This report examines 6 pediatric patients who presented with neurologic deficits associated with the presence of such tumors. These cases illustrate a perplexing phenomenon, where benign teratomas could have a possible association with anti-NMDAR encephalitis. The purpose of this study was to compare the histology and immunohistochemistry of tumors associated with this syndrome to ovarian teratomas found in patients presenting with no neurologic symptoms. After obtaining institutional review board approval, 57 cases of ovarian teratomas were identified at our institution over 12 years. Six patients were identified with anti-NMDAR encephalitis. A panel of immunostains, including S100, GFAP, MAP2, and NeuN was applied to patients' tumor sections as well as the 6 controls from age-matched patients. No qualitative histologic or immunohistochemical differences were seen between the study cases and control group. Because no qualitative differences were identified between the study cases and the control group, testing of paired serum and cerebrospinal fluid remains the best method for diagnosis of anti-NMDAR encephalitis. Tumor banking with molecular analysis of ovarian teratomas, including whole-genome sequencing and comparative genomic hybridization between ovarian tissue saved from patients with and without anti-NMDAR encephalitis, is necessary to fully understand the etiopathogenesis of anti-NMDAR encephalitis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".