Impact of Autoantibody Status on Visual Outcomes in Severe Optic Neuritis Patients Without Multiple Sclerosis
Bibliographic record
Abstract
ABSTRACT Background: Optic neuritis (ON) represents the most common optic neuropathy in young adults; however, longitudinal data on visual recovery, particularly in autoimmune ON subtypes, remain limited. This study aimed to assess long-term visual outcomes in patients with severe ON without multiple sclerosis stratified by autoantibody status: aquaporin-4 (AQP4)-IgG positive, myelin oligodendrocyte glycoprotein (MOG)-IgG positive and double seronegative (DN). Methods: A retrospective cohort analysis was conducted at a tertiary neurology center in southern India, including severe ON patients (best-corrected visual acuity [BCVA] ≤1.0 logMAR) between January 2016 and April 2024. Serological testing for AQP4 and MOG antibodies was performed via cell-based assays. Visual outcomes were categorized as “good recovery” (≥66.77% improvement in BCVA) and “complete recovery” (return to baseline BCVA). Results: Among 42 patients, 17 were AQP4-IgG positive, 10 MOG-IgG positive and 15 DN. The median BCVA at nadir was 1.7 logMAR. Compared with that in the MOG-IgG group, the likelihood of complete visual recovery was lower in both the AQP4-IgG (hazard ratio [HR]: 0.18; p = 0.16) and DN (HR: 0.56; p = 0.34) groups. For good recovery, the AQP4-IgG (HR: 0.16; p = 0.001) and DN (HR: 0.24; p = 0.001) groups had significantly lower HR. All MOG-IgG–positive patients achieved good recovery, compared with fewer than half in the other groups. Conclusion: Antibody status predicted long-term visual outcomes in patients with isolated ON, with MOG-IgG conferring the best recovery, AQP4-IgG the worst and DN intermediate, underscoring the importance of early, antibody-guided management.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".