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 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 | 0.009 | 0.008 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".