Intramuscular Interferon Beta-1A Therapy Initiated during a First Demyelinating Event in Multiple Sclerosis
Bibliographic record
Abstract
BACKGROUND: Treatment with interferon beta has been shown to help patients with established multiple sclerosis, but it is not known whether initiating treatment at the time of a first clinical demyelinating event is of value. METHODS: We conducted a randomized, double-blind trial of 383 patients who had a first acute clinical demyelinating event (optic neuritis, incomplete transverse myelitis, or brain-stem or cerebellar syndrome) and evidence of prior subclinical demyelination on magnetic resonance imaging (MRI) of the brain. After initial treatment with corticosteroids, 193 patients were randomly assigned to receive weekly intramuscular injections of 30 microg of interferon beta-1a and 190 were assigned to receive weekly injections of placebo. The study end points were the development of clinically definite multiple sclerosis and changes in findings on MRI of the brain. The trial was stopped after a preplanned interim efficacy analysis. RESULTS: During three years of follow-up, the cumulative probability of the development of clinically definite multiple sclerosis was significantly lower in the interferon beta-1a group than in the placebo group (rate ratio, 0.56; 95 percent confidence interval, 0.38 to 0.81; P=0.002). As compared with the patients in the placebo group, patients in the interferon beta-1a group had a relative reduction in the volume of brain lesions (P<0.001), fewer new or enlarging lesions (P<0.001), and fewer gadolinium-enhancing lesions (P<0.001) at 18 months. CONCLUSIONS: Initiating treatment with interferon beta-1a at the time of a first demyelinating event is beneficial for patients with brain lesions on MRI that indicate a high risk of clinically definite multiple sclerosis.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".