Prediction of on-treatment disability worsening in RRMS with the MAGNIMS score
Bibliographic record
Abstract
Background: The magnetic resonance imaging in multiple sclerosis (MAGNIMS) score combines relapses and magnetic resonance imaging (MRI) lesions to predict disability outcomes in relapsing–remitting multiple sclerosis (RRMS) treated with interferon-β. Objective: To validate the MAGNIMS score and extend to other disease-modifying therapies (DMTs). To examine the prognostic value of gadolinium contrast-enhancing (Gd+) lesions. Methods: This RRMS MSBase cohort study ( n = 2293) used a Cox model to examine the prognostic value of relapses, MRI activity and the MAGNIMS score for disability worsening during treatment with interferon-β and three other DMTs. Results: Three new T2 lesions (hazard ratio (HR) = 1.60, p = 0.028) or two relapses (HR = 2.24, p = 0.002) on interferon-β (for 12 months) were predictive of disability worsening over 4 years. MAGNIMS score = 2 (1 relapse and ⩾3 T2 lesions or ⩾2 relapses) was associated with a greater risk of disability worsening on interferon-β (HR = 2.0, p = 0.001). In pooled cohort of four DMTs, similar associations were seen (MAGNIMS score = 2: HR = 1.72, p = 0.001). Secondary analyses demonstrated that the addition of Gd+ to the MAGNIMS did not materially improve its prediction of disability worsening. Conclusion: We have validated the MAGNIMS score in RRMS and extended its application to three other DMTs: 1 relapse and ⩾3 T2 lesions or ⩾2 relapses predicted worsening of disability. Contrast-enhancing lesions did not substantially improve the prognostic score.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".