Analysis of clinical outcomes according to original treatment groups 16 years after the pivotal IFNB-1b trial
Bibliographic record
Abstract
BACKGROUND: Evidence for efficacy of disease-modifying drugs in multiple sclerosis (MS) comes from trials of short duration. We report results from a 16 y, retrospective follow-up of the pivotal interferon beta-1b (IFNB-1b) study. METHODS: The 372 trial patients were randomly assigned to placebo (n=123), IFNB-1b 50 microg (n=125) or IFNB-1b 250 microg (n=124) subcutaneously every other day for at least 2 y. Some remained randomised for up to 5 y but, subsequently, patients received treatment according to physicians' discretion. Patients were re-contacted and asked to participate. Efficacy related measures included MRI parameters, relapse rate, the Expanded Disability Status Scale, the Multiple Sclerosis Functional Composite Measure and conversion to secondary progressive MS. RESULTS: Of the 88.2% (328/372) of patients who were identified, 69.9% (260/372) had available case report forms. No differences in outcome between original randomisation groups could be discerned using standard disability and MRI measures. However, mortality rates among patients originally treated with IFNB-1b were lower than in the original placebo group (18.3% (20/109) for placebo versus 8.3% (9/108) for IFNB-1b 50 microg and 5.4% (6/111) for IFNB-1b 250 microg). CONCLUSIONS: The original treatment assignment could not be shown to influence standard assessments of long-term efficacy. On-study behaviour of patients was influenced by factors that could not be controlled with the sacrifice of randomisation and blinding. Mortality was higher in patients originally assigned to placebo than those who had received IFNB-1b 50 microg or 250 microg. The dataset provides important resources to explore early predictors of long-term outcome.
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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 | 0.019 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".