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 mm3 (Quartile 1, 0.0; Quartile 3, 173.4) vs 0.0 mm3 (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 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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".