Infection risk with alemtuzumab decreases over time: pooled analysis of 6-year data from the CAMMS223, CARE-MS I, and CARE-MS II studies and the CAMMS03409 extension study
Bibliographic record
Abstract
BACKGROUND: Reduced MS disease activity with alemtuzumab versus subcutaneous interferon beta-1a (SC IFNB-1a) in core phase 2/3 studies was accompanied by increased incidence of infections that were mainly nonserious and responsive to treatment. Alemtuzumab efficacy was durable over 6 years. OBJECTIVE: To evaluate infections over 6 years in alemtuzumab-treated patients. METHODS: Three randomized trials (CAMMS223, Comparison of Alemtuzumab and Rebif Efficacy in Multiple Sclerosis (CARE-MS) I, and CARE-MS II) compared two courses of alemtuzumab 12 mg with SC IFNB-1a 44 μg in patients with active relapsing-remitting MS. An extension study (CAMMS03409) provided further evaluation and as-needed alemtuzumab retreatment. RESULTS: Infections occurred more frequently with alemtuzumab 12 mg than SC IFNB-1a during Years 1 (58.7% vs 41.3%) and 2 (52.6% vs 37.7%), but declined for alemtuzumab-treated patients in Years 3 (46.6%), 4 (42.8%), 5 (40.9%), and 6 (38.1%). Serious infections were uncommon (1.0%-1.9% per year). Infections were predominantly (>95%) mild to moderate and included upper respiratory tract infections, urinary tract infections, and mucocutaneous herpetic infections. Prophylactic acyclovir reduced herpetic infections. Lymphocyte counts after alemtuzumab therapy did not predict infection risk. CONCLUSION: Infections with alemtuzumab were mostly mild to moderate and decreased over time, consistent with preservation of components of protective immunity.
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 | 0.024 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.015 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 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".