MAGNIMS score predicts long-term clinical disease activity-free status and confirmed disability progression in patients treated with subcutaneous interferon beta-1a
Bibliographic record
Abstract
BACKGROUND: Subcutaneous (sc) interferon (IFN) β-1a reduces relapse rates and delays disability progression in patients with MS. We examined the association of the year 1 Magnetic Resonance Imaging in MS (MAGNIMS) score with long-term clinical disease activity (CDA) -free status and confirmed disability progression in patients treated with sc IFN β-1a in PRISMS. METHODS: Patients treated with sc IFN β-1a three-times-weekly (22 or 44 μg; pooled data) were classified by MAGNIMS score (0, n = 129; 1, n = 108; 2, n = 130) at year 1. Hazard ratios (HR; 95% confidence intervals [CI]) for risk of CDA and confirmed Expanded Disability Status Score (EDSS) progression were calculated by MAGNIMS score for up to 15 years of follow-up. RESULTS: The risk of CDA was higher with a year 1 MAGNIMS score of 1 versus 0 (HR 1.82 [1.38-2.41]), 2 versus 0 (2.63 [2.01-3.45]) and 2 versus 1 (1.45 [1.11-1.89], all p < 0.0001). The same outcome was observed with the risk of confirmed EDSS progression (1 versus 0: 1.93 [1.23-3.02]; 2 versus 0: 2.95 [1.95-4.46]; 2 versus 1: 1.53 [1.05-2.23]; all p < 0.0001). CONCLUSION: In PRISMS, MAGNIMS score at Year 1 predicted risk of CDA and confirmed disability progression in sc IFN β-1a-treated patients over up to 15 years. PRISMS-15 clinicaltrial.gov identifier: NCT01034644.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".