Immunotherapy prevents long-term disability in relapsing multiple sclerosis over 15 years
Bibliographic record
Abstract
ABSTRACT Objective Whether immunotherapy improves long-term disability in multiple sclerosis has not been satisfactorily demonstrated. This study examined the effect of immunotherapy on long-term disability outcomes in relapsing-remitting multiple sclerosis. Methods We studied patients from MSBase followed for ≥1 year, with ≥3 visits, ≥1 visit per year and exposed to a multiple sclerosis therapy, and a subset of patients with ≥15-year follow-up. Marginal structural models were used to compare the hazard of 12-month confirmed increase and decrease in disability, EDSS step 6 and the incidence of relapses between treated and untreated periods. Marginal structural models were continuously re-adjusted for patient age, sex, pregnancy, date, disease course, time from first symptom, prior relapse history, disability and MRI activity. Results 14,717 patients were studied. During the treated periods, patients were less likely to experience relapses (hazard ratio 0.60, 95% confidence interval 0.43–0.82, p=0.0016), worsening of disability (0.56, 0.38-0.82, p=0.0026) and progress to EDSS step 6 (0.33, 0.19-0.59, p=0.00019). Among 1085 patients with ≥15-year follow-up, the treated patients were less likely to experience relapses (0.59, 0.50–0.70, p=10 -9 ) and worsening of disability (0.81, 0.67-0.99, p=0.043). Conclusions Continued treatment with multiple sclerosis immunotherapies reduces disability accrual (by 19-44%), the risk of need of a walking aid by 67% and the frequency of relapses (by 40-41%) over 15 years. A proof of long-term effect of immunomodulation on disability outcomes is the key to establishing its disease modifying properties.
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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 | 0.003 |
| 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 | 0.000 |
| 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".