A Practical Approach to the Diagnosis and Management of Optic Neuritis
Bibliographic record
Abstract
Optic neuritis (ON), as an umbrella term, refers to a spectrum of inflammatory optic neuropathies arising from a myriad of potential causes. In its most common form, "typical" ON presents as a unilateral, painful subacute vision loss event in young Caucasian women. The Optic Neuritis Treatment Trial (ONTT) has historically guided our treatment of ON, and taught us important lessons about the clinical presentation, visual prognosis, and future risk of multiple sclerosis (MS) diagnosis associated with this condition. However, in the decades since the ONTT, several immune-mediated conditions such as neuromyelitis optica spectrum disorder (NMOSD), and myelin-oligodendrocyte glycoprotein IgG associated disease (MOGAD) have been discovered, complicating the clinical approach to ON. Unlike MS, other central nervous system (CNS) inflammatory conditions are associated with ON subtypes that are potentially blinding, and prone to recurrence. Owing to differences in the clinical presentation, serological biomarkers, radiological findings, and prognostic implications associated with MS ON, NMOSD ON, and MOGAD ON subtypes, it is imperative that clinicians be aware of the diagnostic approach and management options for these conditions.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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".