Timing and Predictors of T2-Lesion Resolution in Patients With Myelin Oligodendrocyte Glycoprotein Antibody–Associated Disease
Bibliographic record
Abstract
Objectives To determine the timing and predictors of T2-lesion resolution in myelin oligodendrocyte glycoprotein antibody–associated disease (MOGAD). Methods This retrospective observational study using standard-of-care data had inclusion criteria of MOGAD diagnosis, ≥2 MRIs 12 months apart, and ≥1 brain/spinal cord T2-lesion. The median (interquartile range [IQR]) number of MRIs (82% at disease onset) per-patient were: brain, 5 (2–8); spine, 4 (2–8). Predictors of T2-lesion resolution were assessed with age- and sex-adjusted generalized estimating equations and stratified by T2-lesion size (small <1 cm; large ≥1 cm). Results We studied 583 T2-lesions (brain, 512 [88%]; spinal cord, 71 [12%]) from 55 patients. At last MRI (median follow-up 54 months [IQR 7–74]) 455 T2-lesions (78%) resolved. The median (IQR) time to resolution was 3 months (1.4–7.0). Small T2-lesions resolved more frequently and faster than large T2-lesions. Acute T1-hypointensity decreased the likelihood (odds ratio [95% CI]) of T2-lesion resolution independent of size (small: 0.23 [0.09–0.60], p = 0.002; large: 0.30 [0.16–0.55], p < 0.001), whereas acute steroids favored resolution of large T2-lesions (1.75 [1.01–3.03], p = 0.046). Notably, 32/55 (58%) T2-lesions resolved without treatment. Discussion The high frequency of spontaneous T2-lesion resolution suggests that this represents MOGAD9s natural history. The speed of T2-lesion resolution and influence of size, corticosteroids, and T1-hypointensity on this phenomenon gives insight into MOGAD pathogenesis.
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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 | 0.003 |
| 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 | 0.000 |
| 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".