How to avoid missing a diagnosis of neuromyelitis optica spectrum disorder
Bibliographic record
Abstract
Recognizing neuromyelitis optica spectrum disorder (NMOSD) and differentiating NMOSD from multiple sclerosis (MS) and other disorders can be challenging yet it is extremely important to prevent misdiagnosis, defined in this review as the incorrect diagnosis of patients who truly have NMOSD, particularly in aquaporin-4-IgG (AQP4-IgG)-seronegative cases. The heterogeneity of clinical presentations and wide range of differential diagnoses often lead to missed diagnoses of NMOSD. Misapplication of the 2015 NMOSD criteria and misinterpretation of clinical and neuroradiological findings are relevant factors associated with misdiagnosis in clinical practice. Despite the presence of a specific biomarker for NMOSD (AQP4-IgG), misdiagnosis rates have been reported as high as 35%. Studies indicate that misdiagnosed patients often undergo unnecessary prolonged immunotherapy, leading to health risks and increased morbidity. Accurate definitive diagnosis is crucial as long-term outcomes and treatment approaches differ based on the correct diagnosis, and inappropriate immunotherapy can lead to disability in NMOSD patients. This review outlines factors linked to NMOSD misdiagnosis and briefly discusses strategies to reduce 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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".