Ten-year follow-up of the ‘minimal MRI lesion’ subgroup from the original CHAMPS Multiple Sclerosis Prevention Trial
Bibliographic record
Abstract
BACKGROUND: Patients with clinically isolated syndrome (CIS) and characteristic magnetic resonance imaging (MRI) lesions are at high risk for multiple sclerosis (MS). However, patients with a minimal MRI lesion burden (a low T2-hyperintense [low T2] lesion count) may have borderline formal diagnostic criteria, presenting a clinical management challenge. OBJECTIVE: Compare the 10-year disease progression of patients with low and higher T2 lesion counts treated over most intervals. METHODS: CIS patients from the original CHAMPS MS trial were retrospectively assigned to low-T2 (first quartile; 2-8 lesions) or higher-T2 (second through fourth quartiles; ≥ 9 lesions) groups using baseline T2 lesion counts. The 5- and 10-year open-label extension of CHAMPS (CHAMPIONS) evaluated conversion to clinically definite MS (CDMS), MRI activity, relapses, and disability. RESULTS: The vast majority of patients showed new disease activity by MRI and/or clinical criteria at 10 years (low-T2 86%; higher-T2 98%). Fewer low-T2 than higher-T2 patients developed CDMS (40% vs. 63%; p = 0.013); low-T2 patients also had fewer new brain lesions, less brain volume loss, and less disability progression. CONCLUSION: CIS patients with low T2 lesion counts show continued disease activity. However, all assessments of disease progression over 10 years indicated a significantly less severe disease course for low-T2 patients.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".