Diffusely Abnormal White Matter, T<sub>2</sub> Burden of Disease, and Brain Volume in Relapsing‐Remitting Multiple Sclerosis
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Multiple sclerosis (MS) diffusely abnormal white matter (DAWM) is a mildly hyperintense magnetic resonance imaging abnormality distinct from typical lesions. Our goal was to investigate the prevalence and natural history of DAWM in a large cohort (n = 348) of relapsing-remitting MS (RRMS) patients. METHODS: burden of disease (BOD), brain volume (brain fractional ratio, BFR), and disability (Expanded Disability Status Scale, EDSS) were investigated at baseline and year 7-8 (long-term follow-up, LTF). RESULTS: DAWM was present in 25.3% (88 of 348) of patients at baseline. At LTF, DAWM was unchanged in 69.3% (61 of 88), decreased in 28.4% (25 of 88), and increased in 2.3% (2 of 88) of patients. Baseline BOD and change in BOD did not significantly differ between patients with and without DAWM. DAWM was associated with greater reduction in BFR at LTF (P = .038). DAWM and DAWM change did not predict EDSS or EDSS progression. CONCLUSIONS: DAWM is present in a quarter of RRMS patients, and rarely increases or develops de novo. DAWM predicts brain atrophy but does not predict physical disability. Because of its posterior periventricular location, further investigation is warranted to evaluate its relationship to other measures of disability, including visual spatial processing and cognitive function.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| 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".