Early MRI results and odds of attaining ‘no evidence of disease activity’ status in MS patients treated with interferon β-1a in the EVIDENCE study
Bibliographic record
Abstract
INTRODUCTION: 'No evidence of disease activity' (NEDA) is increasingly used as a treatment target with disease-modifying drugs for relapsing multiple sclerosis. METHODS: This post-hoc analysis of the randomised EVIDENCE trial compared interferon beta-1a injected subcutaneously three times weekly (IFN β-1a SC tiw) with interferon β-1a injected intramuscularly once weekly (IFN β-1a IM qw) on NEDA and clinical activity-free (CAF) status. The influence of the frequency of magnetic resonance imaging (MRI) scanning on NEDA and the effect of baseline T1 gadolinium-enhancing (Gd+) lesions on NEDA and CAF were also investigated. RESULTS: More patients in the IFN β-1a SC tiw group achieved NEDA compared with the IFN β-1a IM qw group, although rates were lower when monthly MRI scans through 24weeks were included (35.0% vs. 21.6%, respectively; p<0.001) versus the 24-week scan alone (59.5% vs. 41.2%; p<0.001). Absence of baseline Gd+ lesions predicted NEDA through Week 72 in the IFN β-1a IM qw group (p=0.022), and CAF through Week 48 in patients receiving IFN β-1a SC tiw (p=0.024). CONCLUSIONS: IFN β-1a SC tiw was associated with significantly higher rate of NEDA status compared with IFN β-1a IM qw. Baseline Gd+ lesions augured less frequent CAF or NEDA status. Inclusion of more MRI scans in the analysis reduced rates of NEDA status.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".