Impact of exposure to interferon beta-1a on outcomes in patients with relapsing–remitting multiple sclerosis: exploratory analyses from the PRISMS long-term follow-up study
Bibliographic record
Abstract
OBJECTIVE: To explore the effects of exposure to subcutaneous (sc) interferon (IFN) beta-1a on efficacy in patients with relapsing-remitting multiple sclerosis (RRMS) enrolled in the PRISMS (Prevention of Relapses and disability by Interferon beta-1a Subcutaneously in Multiple Sclerosis) study. METHODS: Patients with RRMS received IFN beta-1a, 44 or 22 µg sc three times weekly (tiw), or placebo, for 2 years, at which point placebo recipients were re-randomized to IFN beta-1a, 44 or 22 µg sc tiw, for a further 2-4 years. Long-term follow-up visits occurred 7-8 years after enrolment and allowed participation of patients who had previously discontinued treatment. Post hoc descriptive analyses were conducted within the lower (MIN) and upper (MAX) quartiles of patients divided according to cumulative dose of IFN beta-1a and cumulative time on treatment. Outcomes were explored in patients initially randomized to IFN beta-1a, 44 µg sc tiw, who had received continuous or noncontinuous therapy during the study. RESULTS: For both cumulative dose and time analyses, the MIN and MAX groups comprised 96 and 95 patients, respectively. The continuous and noncontinuous groups included 45 and 91 patients, respectively. The MAX DOSE and MAX TIME groups had lower annualized relapse rates, lower rates of conversion to secondary progressive MS, lower percentages of patients with Expanded Disability Status Scale progression, higher percentages of relapse-free patients, and less T2 burden of disease than the MIN groups. The continuous therapy group had a lower annualized relapse rate and lower percentages of patients with Expanded Disability Status Scale progression or conversion to secondary progressive MS than the noncontinuous therapy group. CONCLUSIONS: The findings of these post hoc analyses suggest that high exposure to sc IFN beta-1a may be associated with better clinical outcomes than low exposure, and also highlight the importance of maximizing adherence. Additional prospective investigation is warranted to evaluate further the effects of treatment exposure on outcomes and to determine the benefits of interventions to improve adherence.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".