Serum MOG-IgG in children meeting multiple sclerosis diagnostic criteria
Bibliographic record
Abstract
BACKGROUND: Myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD) is now recognized as distinct from multiple sclerosis (MS). OBJECTIVE: To evaluate the importance of considering myelin oligodendrocyte glycoprotein (MOG)-immunoglobulin-G (IgG) serology when applying MS diagnostic criteria in children. METHODS: Within a prospective cohort of children meeting MS criteria (median follow-up = 6 years, interquartile range (IQR) = 4-9), we measured MOG-IgG in serial archived serum obtained from presentation, and compared imaging and clinical features between seropositive and seronegative participants. RESULTS: Of 65 children meeting MS criteria (median age = 14.0 years, IQR = 10.9-15.1), 12 (18%) had MOG-IgG at disease onset. Seropositive participants were younger, had brain magnetic resonance imaging (MRI) features atypical for MS, rarely had cerebrospinal fluid (CSF) oligoclonal bands (2/8, 25%), and accumulated fewer T2 lesions over time. On serial samples, 5/12 (42%) were persistently seropositive, 5/12 (42%) became seronegative, and 2/12 (17%) had fluctuating results. All 12 children experienced a disease course different from typical MS. CONCLUSION: While children with MOG-IgG can have clinical, CSF, and MRI features conforming to MS criteria, the presence of MOG-IgG is associated with atypical features and predicts a non-MS disease course. Given MOG-IgG seropositivity can wane over time, testing at first attack is of considerable importance for the diagnosis of MOGAD.
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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 | 0.005 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".