Subcutaneous interferon β-1a three times weekly and the natural evolution of gadolinium-enhancing lesions into chronic black holes in relapsing and progressive multiple sclerosis: Analysis of PRISMS and SPECTRIMS trials
Bibliographic record
Abstract
Background Evolution of gadolinium-enhancing lesions into chronic black holes (CBH) may be reduced by interferon (IFN) therapy. Objective The objective of this paper is to assess the effect of IFN β-1a and placebo on CBH evolution and disability in patients with relapsing–remitting multiple sclerosis (RRMS), as well as CBH evolution in patients with secondary progressive multiple sclerosis (SPMS). Methods A post hoc, exploratory analysis of patients with RRMS and SPMS with monthly MRI scans (months –1 to 9) from two separate placebo-controlled clinical trials of IFN β-1a was conducted. Results In RRMS patients, the risk of ≥1 evolved CBH was lower for IFN β-1a versus placebo (odds ratio 0.42; p = 0.024); volume of newly evolved CBH was numerically reduced. A numerically higher proportion of patients with ≥1 evolving CBH vs no evolving CBH had confirmed three-month disability progression (four-year rate 55.8% vs 43.1%, respectively). Proportion of lesions evolving into CBH (patient level: 34.7% vs 12.6%, p < 0.0001; lesion level: 28.8% vs 11.0%, p < 0.0001) and evolved CBH volume (median 33.5 mm 3 (Quartile 1, 0.0; Quartile 3, 173.4) vs 0.0 mm 3 (0.0; 52.4); p = 0.0008) was higher for SPMS than RRMS patients treated with IFN β-1a. Conclusion In RRMS, IFN β-1a significantly decreased the proportion of new T1 Gd+ lesions evolving into CBH and the risk of developing a CBH. In patients with SPMS, more lesions develop to CBH, indicating reduced repair capacity, and the natural history of lesion development appears to be unaffected by IFN β-1a treatment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".