Non‐demyelinating disorders mimicking and misdiagnosed as<scp>NMOSD:</scp>a literature review
Bibliographic record
Abstract
BACKGROUND: Differentiating neuromyelitis optica spectrum disorder (NMOSD) from its mimics is crucial to avoid misdiagnosis, especially in the absence of aquaporin-4-IgG. While multiple sclerosis (MS) and myelin oligodendrocyte glycoprotein-IgG associated disease (MOGAD) represent major and well-defined differential diagnoses, non-demyelinating NMOSD mimics remain poorly characterized. METHODS: We conducted a systematic review on PubMed/MEDLINE to identify reports of patients with non-demyelinating disorders that mimicked or were misdiagnosed as NMOSD. Three novel cases seen at the authors' institutions were also included. The characteristics of NMOSD mimics were analyzed and red flags associated with misdiagnosis identified. RESULTS: A total of 68 patients were included; 35 (52%) were female. Median age at symptoms onset was 44 (range, 1-78) years. Fifty-six (82%) patients did not fulfil the 2015 NMOSD diagnostic criteria. The clinical syndromes misinterpreted for NMOSD were myelopathy (41%), myelopathy + optic neuropathy (41%), optic neuropathy (6%), or other (12%). Alternative etiologies included genetic/metabolic disorders, neoplasms, infections, vascular disorders, spondylosis, and other immune-mediated disorders. Common red flags associated with misdiagnosis were lack of cerebrospinal fluid (CSF) pleocytosis (57%), lack of response to immunotherapy (55%), progressive disease course (54%), and lack of magnetic resonance imaging gadolinium enhancement (31%). Aquaporin-4-IgG positivity was detected in five patients by enzyme-linked immunosorbent assay (n = 2), cell-based assay (n = 2: serum, 1; CSF, 1), and non-specified assay (n = 1). CONCLUSIONS: The spectrum of NMOSD mimics is broad. Misdiagnosis frequently results from incorrect application of diagnostic criteria, in patients with multiple identifiable red flags. False aquaporin-4-IgG positivity, generally from nonspecific testing assays, may rarely contribute to misdiagnosis.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| 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".