Efficacy of intramuscular interferon beta-1a in patients with clinically isolated syndrome: analysis of subgroups based on new risk criteria
Bibliographic record
Abstract
Approximately 85% of multiple sclerosis (MS) cases begin as clinically isolated syndromes (CIS). Results from the Controlled High-Risk Subjects Avonex((R)) Multiple Sclerosis Prevention Study (CHAMPS) demonstrated that, in patients with CIS, treatment with intramuscular (IM) interferon beta-1a (IFNbeta-1a) 30 mug once weekly delayed conversion to clinically definite MS (CDMS) in the total population and in subgroups based on presenting syndromes and baseline magnetic resonance imaging (MRI) characteristics. Changes to clinical and MRI risk classification of presenting symptoms in recent studies prompted reanalysis of CHAMPS data. Presenting syndromes were assessed using a derived algorithm that stratifies patients into mono- or multifocal categories based on functional system scores. The ability of IM IFNbeta-1a to delay progression to CDMS in subgroups based on clinical presentation and MRI characteristics was assessed. Reanalysis of CHAMPS patients showed that 30% could be classified by clinical criteria as having multifocal disease at baseline. IM IFNbeta-1a initiated at a first demyelinating attack delayed CDMS in monofocal patients (P = 0.0013), patients with or without gadolinium-enhancing lesions (P = 0.0007, P = 0.0405) and patients with at least nine T2 lesions at baseline (P = 0.0044). These data confirm that IM IFNbeta-1a delays conversion to CDMS in patients with CIS.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".