Clinical and radiologic approach to ‘typical’ versus antibody-related optic neuritis
Bibliographic record
Abstract
PURPOSE OF REVIEW: Optic neuritis is an autoimmune optic neuropathy that has been associated with multiple sclerosis (MS), neuromyelitis optica spectrum disorder (NMOSD), and more recently antimyelin oligodendrocyte glycoprotein (anti-MOG)-positive disorder. At initial presentation, it is often difficult to differentiate these entities given their significant overlap in clinical presentation and MRI findings. This review summarizes the distinguishing clinical and radiological features of MS, NMOSD, and anti-MOG disorders to help clinicians accurately diagnose and manage patients affected by these conditions. RECENT FINDINGS: Antiaquaporin-4 (AQP4) and more recently anti-MOG antibodies are both associated with central nervous system demyelinating diseases that often initially present with optic neuritis. Serologic testing now allows for a new classification of these overlapping conditions that can help to differentiate 'typical' optic neuritis that is often associated with MS from 'atypical' optic neuritis associated with NMOSD and anti-MOG-positive disorder. SUMMARY: Optic neuritis associated with MS, NMOSD, and anti-MOG-positive disease can have a similar clinical presentation. However, some clinical and radiologic findings can help clinicians to differentiate these entities so that they can be properly managed to optimize visual prognosis.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".