Myelin Oligodendrocyte Glycoprotein Antibody-Associated Optic Neuritis in Canada
Bibliographic record
Abstract
OBJECTIVE: To describe clinical characteristics of Canadian patients with myelin-oligodendrocyte glycoprotein immunoglobulin-G optic neuritis (MOG-IgG ON). METHODS: Retrospective observational case series of MOG-IgG seropositive patients with ON referred to tertiary neuro-ophthalmology practices. Outcome measures included clinical characteristics, radiologic findings, and visual outcomes. RESULTS: Forty-six eyes of 30 patients were included. Twenty-three (76.7%) were women, mean onset age was 40.7 years (range 16-77), and most were Caucasian. Seventeen (56.7%) presented with their first ON episode. Sixteen (53.3%) had bilateral eye involvement. Isolated ON without associated neurological symptoms occurred in 90.0%. In 22 patients with acute ON (seen within 1 month of onset), presenting mean visual acuity (VA) was 20/258 (logMAR 1.11), mean deviation (MD) on Humphrey visual fields was -16.90 ± 10.83 dB, and peripapillary retinal nerve fiber layer (RNFL) thickness on ocular coherence tomography (OCT) was 164.23 ± 46.53 um. Orbital magnetic resonance imaging (MRI) within 1 month of symptom onset for 19 patients demonstrated orbital optic nerve enhancement in 11 (57.9%) and perineural enhancement in 11 (57.9%). Brain MRI was normal in 28 (93.3%) patients. Twenty out of 22 patients with acute presentation were treated with high-dose glucocorticoids and 5 with plasma exchange in addition to corticosteroids. Long-term immunosuppression was utilized in 9 (30%) out of all 30 patients. Final VA was 20/30 (logMAR 0.18), MD was -7.17 ± 8.85 dB, and RNFL thickness was 72.15 ± 20.16 um. CONCLUSION: MOG-IgG ON in Canada has a variable presentation with most patients having substantial initial vision loss with good recovery. This is the largest characterization of the disease in Canada to date.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".