Long-term clinical outcomes in patients with CIS treated with interferon beta-1b: results from the 15-year follow up of the BENEFIT trial
Bibliographic record
Abstract
Multiple sclerosis (MS) treatment intervention with immunomodulating therapy at early disease stage improves short term clinical outcomes. The objective of this study is to describe the long-term outcomes and healthcare utilization of patients with clinically isolated syndrome (CIS) included in the Betaferon®/Betaseron® in Newly Emerging MS for Initial Treatment (BENEFIT) randomized, parallel group trial. In BENEFIT patients were assigned to "early" IFNB-1b treatment or placebo ("delayed" treatment). After 2 years or conversion to clinically definite multiple sclerosis (CDMS), all patients were offered IFNB-1b and were reassessed 15 years later. Of 468 patients, 261 (55.8%) were enrolled into BENEFIT 15 (161 [55.1%] from the early, 100 [56.8%] from the delayed treatment arm). In the full BENEFIT analysis set, risk of conversion to CDMS remained lower in the early treatment group ( - 30.5%; hazard ratio 0.695 [95% CI, 0.547-0.883]; p = 0.0029) with a 15.7% lower risk of relapse than in the delayed treatment group (p = 0.1008). Overall, 25 patients (9.6%; 9.9% early, 9.0% delayed) converted to secondary progressive multiple sclerosis. Disability remained low and stable with no significant difference between groups in Expanded Disability Status Scale score or MRI metrics. Paced Auditory Serial Addition Task-3 scores were better in the early treatment group (p = 0.0036 for treatment effect over 15 years). 66.3% of patients were still employed at Year 15 versus 74.7% at baseline. In conclusion, results 15 years from initial randomization support long-term benefits of early treatment with IFNB-1b.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".