Optic neuritis: current challenges in diagnosis and management
Bibliographic record
Abstract
PURPOSE OF REVIEW: The primary aim of this review is to describe the clinical course, salient imaging features, and relevant serological profiles of common optic neuritis (ON) subtypes. Key diagnostic challenges and treatment options will also be discussed. RECENT FINDINGS: ON is a broad term that describes an inflammatory optic nerve injury arising from a variety of potential causes. ON can occur sporadically, however there is particular concern for co-associated central nervous system (CNS) inflammatory syndromes including multiple sclerosis (MS), neuromyelitis optic spectrum disorders (NMOSD), and myelin oligodendrocyte glycoprotein antibody associated disease (MOGAD). The ON subtypes that often herald MS, NMOSD, and MOGAD differ with respect to serological antibody profile and neuroimaging characteristics, yet there is significant overlap in their clinical presentations. A discerning history and thorough examination are critical to rendering the correct diagnosis. SUMMARY: Optic neuritis subtypes vary with respect to their long-term prognosis and accordingly, require different acute treatment strategies. Moreover, delays in identifying MOGAD, and certainly NMOSD, can be highly detrimental because affected individuals are vulnerable to permanent vision loss and neurologic disability from relapses.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".