Timing and Predictors of T2-Lesion Resolution in Patients With Myelin Oligodendrocyte Glycoprotein Antibody–Associated Disease
Bibliographic record
Abstract
<h3>Objectives</h3> To determine the timing and predictors of T2-lesion resolution in myelin oligodendrocyte glycoprotein antibody–associated disease (MOGAD). <h3>Methods</h3> 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). <h3>Results</h3> 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], <i>p</i> = 0.002; large: 0.30 [0.16–0.55], <i>p</i> < 0.001), whereas acute steroids favored resolution of large T2-lesions (1.75 [1.01–3.03], <i>p</i> = 0.046). Notably, 32/55 (58%) T2-lesions resolved without treatment. <h3>Discussion</h3> 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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".