Acute Myelin Oligodendrocyte Glycoprotein-IgG Optic Neuritis without Optic Nerve Enhancement
Bibliographic record
Abstract
Introduction: Optic neuritis (ON) is a common neuro-ophthalmological condition and remains a significant indicator of inflammatory conditions affecting the central nervous system. Varying etiologies exist for ON including multiple sclerosis, NMOSD, and MOGAD. Differential diagnosis is achieved using both radiological and serological testing. MRI characteristics of MOG-ON include T2 hypersensitive lesions, nerve swelling, and gadolinium enhancement of the affected optic nerve. While acute MOG-ON usually presents with optic nerve enhancement, recognizing atypical presentations is critical in accurate diagnosis and effective management. Case Presentation: We herein present a case of a 67-year-old woman presenting with sudden decrease in vision in the right eye. The patient underwent a 3T MRI of the orbits and brain 5 days post vision loss which returned normal right optic nerve appearance at presentation (no edema, enhancement or increased T2 signal). Further serological testing of the MOG antibody returned positive (1:100) while APQ4 antibodies were negative. This yielded a diagnosis of MOG-IgG-ON. Subsequently, the patient was treated with IV methylprednisolone 1 g daily for 5 days followed by prednisone 1 mg/kg, resulting in marked improvement in vision. Conclusion: This case highlights the complexity involved in diagnosing ON, especially in the context of MOGAD. Absence of optic nerve enhancement in this patient calls attention to the possibility of subclinical inflammation and/or detectable enhancement later in clinical course. Our findings, along with existing literature, highlight the need for clinicians to consider atypical MRI presentations in MOG-ON cases. Recognizing that normal MRI does not exclude MOG-ON is important for optimizing diagnostic accuracy and effective treatment interventions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".