Lack of Association between Antimyelin Antibodies and Progression to Multiple Sclerosis
Bibliographic record
Abstract
BACKGROUND: Patients with a single episode of neurologic dysfunction and brain magnetic resonance imaging (MRI) scans suggestive of multiple sclerosis are at high risk for clinically definite multiple sclerosis, but the outcome for individual patients is unpredictable. An increased risk of progression to clinically definite multiple sclerosis in patients with serum antibodies against myelin oligodendrocyte glycoprotein (MOG) and myelin basic protein (MBP) has been reported. METHODS: We measured serum anti-MOG and anti-MBP IgG and IgM antibodies in 462 patients with a first clinical event suggestive of multiple sclerosis and at least two clinically silent lesions on brain MRI. The patients were participating in a multicenter trial of treatment with interferon beta-1b. Antibodies were assessed by Western blot analysis at baseline, and the results compared with the time and rate of progression to clinically definite multiple sclerosis or a diagnosis of multiple sclerosis as defined by an international panel (the McDonald criteria). Regular visits were scheduled for the assessment of neurologic impairment and for MRI before treatment and at months 3, 6, 9, 12, 18, and 24. RESULTS: No associations were found between the presence of anti-MOG and anti-MBP IgM and IgG antibodies and progression to clinically definite multiple sclerosis or a diagnosis of multiple sclerosis according to the McDonald criteria, either in the entire cohort or in any subgroups of the study population. CONCLUSIONS: Serum antibodies against MOG and MBP, as detected by Western blot analysis, are not associated with an increased risk of progression to clinically definite multiple sclerosis in patients who have had a clinically isolated syndrome suggestive of multiple sclerosis.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".