Alemtuzumab CARE-MS I 5-year follow-up
Bibliographic record
Abstract
OBJECTIVE: To evaluate 5-year efficacy and safety of alemtuzumab in treatment-naive patients with active relapsing-remitting MS (RRMS) (CARE-MS I; NCT00530348). METHODS: Alemtuzumab-treated patients received treatment courses at baseline and 12 months later; after the core study, they could enter an extension (NCT00930553) with as-needed alemtuzumab retreatment for relapse or MRI activity. Assessments included annualized relapse rate (ARR), 6-month confirmed disability worsening (CDW; ≥1-point Expanded Disability Status Scale [EDSS] score increase [≥1.5 if baseline EDSS = 0]), 6-month confirmed disability improvement (CDI; ≥1-point EDSS decrease [baseline score ≥2.0]), no evidence of disease activity (NEDA), brain volume loss (BVL), and adverse events (AEs). RESULTS: Most alemtuzumab-treated patients (95.1%) completing CARE-MS I enrolled in the extension; 68.5% received no additional alemtuzumab treatment. ARR remained low in years 3, 4, and 5 (0.19, 0.14, and 0.15). Over years 0-5, 79.7% were free of 6-month CDW; 33.4% achieved 6-month CDI. Most patients (61.7%, 60.2%, and 62.4%) had NEDA in years 3, 4, and 5. Median yearly BVL improved over years 2-4, remaining low in year 5 (years 1-5: -0.59%, -0.25%, -0.19%, -0.15%, and -0.20%). Exposure-adjusted incidence rates of most AEs declined in the extension relative to the core study. Thyroid disorder incidences peaked at year 3 and subsequently declined. CONCLUSIONS: Based on these data, alemtuzumab provides durable efficacy through 5 years in the absence of continuous treatment, with most patients not receiving additional courses. CLINICALTRIALSGOV IDENTIFIER: NCT00530348; NCT00930553. CLASSIFICATION OF EVIDENCE: This study provides Class III evidence that alemtuzumab durably improves efficacy outcomes and slows BVL in patients with RRMS.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".