Interferon beta-1b reduces black holes in a randomised trial of clinically isolated syndrome
Bibliographic record
Abstract
BACKGROUND: Multiple sclerosis (MS) is characterised by inflammatory lesions of the central nervous system. Interferon beta-1b (IFNB-1b) has been shown to improve clinical and magnetic resonance imaging (MRI) measures for patients with MS. OBJECTIVE: To evaluate whether IFNB-1b in patients presenting with clinically isolated syndromes (CIS) prevented persisting T1 hypointensities on MRI (persistent black holes (PBHs)). METHODS: In the placebo-controlled phase, patients (n = 468) were initially randomised to IFNB-1b (n = 292) or placebo (n = 176) for two years or clinically definite MS (CDMS). In the open-label phase (n = 418), both groups were offered IFNB-1b for up to five years. Lesions were classified as PBHs if T1 hypointensity persisted throughout the last available scan (minimum time one year). RESULTS: A total of 435 patients were evaluable for analysis. The number of PBHs/patient was lower in the early rather than the delayed treatment arm during both phases (.42 vs .71, p = .0102 and .70 vs 1.17, p = .0121). Exploratory analyses identified baseline characteristics that affected rate of conversion. CONCLUSIONS: Although the rate of lesions that converted to PBH showed no significant differences between groups, the numbers of PBHs per patient out of new lesions was significantly lower in IFNB-1b patients compared to patients on placebo. TRIAL REGISTRATION NUMBER: NCT00544037.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".