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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".