Efficacy of alemtuzumab over 6 years in relapsing–remitting multiple sclerosis patients who relapsed between courses 1 and 2: Post hoc analysis of the CARE-MS studies
Bibliographic record
Abstract
BACKGROUND: Alemtuzumab is administered as two annual courses for relapsing-remitting multiple sclerosis (MS). Patients may relapse before completing the two-course regimen. OBJECTIVE: The objective was to evaluate 6-year outcomes in patients who relapsed between alemtuzumab Courses 1 and 2 (early relapsers). METHODS: Efficacy in Multiple Sclerosis (CARE-MS) studies who enrolled in the extension. RESULTS: Early relapsers (CARE-MS I: 15%; CARE-MS II: 24%) had more relapses in 1-2 years pre-alemtuzumab and higher mean baseline Expanded Disability Status Scale score than patients without relapse. Their annualized relapse rate declined from Year 1 (CARE-MS I: 1.3; CARE-MS II: 1.2) to Year 2 following Course 2 (0.3; 0.5) and remained low thereafter. Over 6 years, 60% remained free of 6-month confirmed disability worsening; 24% (CARE-MS I) and 34% (CARE-MS II) achieved 6-month confirmed disability improvement. During Year 6, 69% (CARE-MS I) and 68% (CARE-MS II) were free of magnetic resonance imaging (MRI) disease activity. Median percent yearly brain volume loss (Year 1: -0.67% (CARE-MS I); -0.47% (CARE-MS II)) declined after Course 2 (Year 6: -0.24%; -0.13%). CONCLUSION: Early relapsers' outcomes improved after completing the second alemtuzumab course. These findings support administering the approved two-course regimen to maximize clinical benefit. CLINICALTRIALS.GOV REGISTRATION NUMBERS: CARE-MS I, II, extension: NCT00530348, NCT00548405, NCT00930553.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".