Efficacy of alemtuzumab in relapsing-remitting MS patients who received additional courses after the initial two courses: Pooled analysis of the CARE-MS, extension, and TOPAZ studies
Bibliographic record
Abstract
BACKGROUND: Alemtuzumab is given as two annual courses. Patients with continued disease activity may receive as-needed additional courses. OBJECTIVE: Efficacy in Multiple Sclerosis) studies and their extensions. METHODS: Subgroups were based on the number of additional alemtuzumab courses received. Exclusion criteria: other disease-modifying therapy (DMT); <12-month follow-up after last alemtuzumab course. RESULTS: In the additional-courses groups, Courses 3 and 4 reduced annualized relapse rate (12 months before: 0.73 and 0.74, respectively; 12 months after: 0.07 and 0.08). For 36 months after Courses 3 and 4, 89% and 92% of patients were free of 6-month confirmed disability worsening, respectively, with 20% and 26% achieving 6-month confirmed disability improvement. Freedom from magnetic resonance imaging (MRI) disease activity increased after Courses 3 and 4 (12 months before: 43% and 53%, respectively; 12 months after: 73% and 74%). Safety was similar across groups; serious events occurred irrespective of the number of courses. CONCLUSION: Additional alemtuzumab courses significantly improved outcomes, without increased safety risks, in CARE-MS patients with continued disease activity after Course 2. How this compares to outcomes if treatment is switched to another DMT instead remains unknown.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.017 |
| Bibliometrics | 0.003 | 0.002 |
| 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".