Magnetic resonance imaging characteristics of myelin oligodendrocyte glycoprotein antibody positive patients -validation of current diagnostic criteria
Bibliographic record
Abstract
BACKGROUND: Magnetic resonance imaging (MRI) features in the 2023 MOGAD International Panel diagnostic criteria help distinguish myelin oligodendrocyte glycoprotein (MOG) antibody disease (MOGAD) from mimics, particularly when MOG antibody titers are low. We evaluated the diagnostic performance of these MRI features in a large, previously diagnosed cohort. METHODS: All MOG IgG cell-based assays performed by Mitogen Dx laboratory in Alberta from July 2017 to July 2023 were retrieved. All MOG positive with a MOGAD presentation and => one MRI study of brain, spine, or optic nerves were identified. MRIs were re-evaluated by two neuroradiologists and a neurologist for MOGAD-like features. Sensitivity, specificity, likelihood ratios, and positive and negative predictive value were calculated based on diagnosis in all patients and low antibody titer patients specifically. RESULTS: Of 3831 tested patients, 158 had MOG antibodies, a MOGAD-consistent presentation, and MRI(s) of brain, spine or optic nerves. Of these 158 patients, 102 were diagnosed with MOGAD. Compared to patients with higher antibody titers, low titer patients with MOGAD MRI features had preserved specificity and improved negative predictive value for a MOGAD diagnosis. Only MOGAD patients had lesion resolution. CONCLUSIONS: When applying MRI features from the 2023 MOGAD diagnostic criteria to an existing cohort, there was good sensitivity and specificity for MOGAD with improved specificity and negative predictive value in those with low antibody titers. The odds of a MOGAD diagnosis were high when lesions resolved on repeated imaging, particularly versus multiple sclerosis, suggesting this feature may merit more weight in future diagnostic criteria.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".