A.03 Durable clinical and MRI efficacy of alemtuzumab over 6 years in CARE-MS II patients with RRMS who relapsed between Courses 1 and 2
Bibliographic record
Abstract
Background: In RRMS patients with inadequate response to prior therapy, 2 alemtuzumab courses (12 mg/day; baseline: 5 days; 12 months later: 3 days) significantly improved outcomes over 2 years (y) versus SC IFNB-1a (CARE-MS II [NCT00548405]), with durable efficacy over a 4-y extension (NCT00930553). We present 6-y efficacy (2-y core study plus 4-y extension) in patients with relapse (relapsers) between Courses (C) 1 and 2. Methods: Annualized relapse rate (ARR); 6-month confirmed disability worsening (CDW); MRI disease activity (Gd-enhancing lesions; new/enlarging T2 hyperintense lesions); brain volume loss (BVL; derived by relative change in brain parenchymal fraction). Results: 105/435 (24%) patients relapsed between C1 and C2; 33% (relapsers) versus 55% without relapse (non-relapsers) received neither alemtuzumab retreatment nor another disease-modifying therapy through Y6. ARR (Y1: 1.2) declined post-C2 (0.5), remaining low through Y6 (0.2 [0.1, non-relapsers]; 10/105 [10%] relapsed). Through Y6, patients remained CDW-free (60% [relapsers]; 75% [non-relapsers]), Gd-enhancing lesion-free (94% [relapsers]; 90% [non-relapsers]), new/enlarging T2 hyperintense lesion-free (68% [relapsers]; 69% [non-relapsers]), and MRI disease activity-free (68% [relapsers]; 69% [non-relapsers]). Alemtuzumab slowed median percent yearly BVL (Y6: -0.13% [relapsers]; -0.10% [non-relapsers]). Conclusions: Patients relapsing between C1 and C2 improved post-C2 through Y6. These findings support administering 2 alemtuzumab courses to achieve optimal and durable benefit.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".