Small Vessel Vasculitis in Biopsies Of Anti-mog Encephalitis
Bibliographic record
Abstract
We report the neuropathology of two pediatric brain biopsy cases associated with serum anti-myelin oligodendrocyte glycoprotein (MOG) positivity. Descriptions of anti-MOG associated neuropathology are limited, with initial reports describing various patterns of inflammatory demyelination. Our first patient presented with confusion, speech abnormalities and personality changes following a treated strep throat infection. Our second patient had a past medical history of neurofibromatosis type 1 (NF1) and presented with hypersomnolence and focal neurological deficits. MRI abnormalities included diffuse scattered T2 FLAIR hyperintensities +/- enhancement. CSF was positive for anti-MOG antibodies in both cases, while one case exhibited additional anti-NMDA-R antibodies. Brain biopsies revealed vasocentric mononuclear inflammation featuring a predominance of lymphocytes that included intramural forms, as well as diffuse microglial activation, but no evidence of microglial nodules or microorganisms. One case demonstrated mild perivascular demyelination. The prevailing pattern in both cases was suggestive of “small vessel childhood primary angiitis of the central nervous system” (SVcPACNS). Our results parallel recent reports of anti-MOG neuropathology describing small vessel vasculitis, contrary to initial and subsequent reports that describe “encephalitis”. The foregoing suggests that the neuropathology associated with serum anti-MOG positivity may be broader than first appreciated. Moreover, this pattern of vasculitis might have implications for the natural history of this nascent disorder. Learning Objectives Define anti-MOG encephalitis. Recognize the pathologic spectrum of reported cases of anti-MOG encephalitis. Contrast the pathologic features of pediatric and adult CNS vasculitis. Describe the histologic overlap of vasculitis and encephalitis.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".