Best Practices for Long-Term Monitoring and Follow-Up of Alemtuzumab-Treated MS Patients in Real-World Clinical Settings
Bibliographic record
Abstract
Multiple sclerosis (MS) is a chronic autoimmune neurological disease that typically affects young adults, causing irreversible physical disability and cognitive impairment. Alemtuzumab, administered intravenously as 2 initial courses of 12 mg/day (5 consecutive days at baseline, and 3 consecutive days 12 months later), resulted in significantly greater improvements in clinical and MRI outcomes vs. subcutaneous interferon beta-1a over 2 years in patients with active relapsing-remitting MS (RRMS) who were either treatment-naive (CARE-MS I; NCT00530348) or had an inadequate response to prior therapy (CARE-MS II; NCT00548405). Efficacy with alemtuzumab was maintained over 7 years in subsequent extension studies (NCT00930553; NCT02255656), in the absence of continuous treatment and with a consistent safety profile. There is an increased incidence of autoimmune events in patients treated with alemtuzumab (mainly thyroid events, but also immune thrombocytopenia and nephropathy), which imparts a need for mandatory safety monitoring for 4 years following the last treatment. The risk management strategy for alemtuzumab-treated patients includes laboratory monitoring and a comprehensive patient education and support program that enables early detection and effective management of autoimmune events, yielding optimal outcomes for MS patients. Here we provide an overview of tools and techniques that have been implemented in real-world clinical settings to reduce the burden of monitoring for both patients and healthcare providers, including customized educational materials, the use of social media, and interactive online databases for managing healthcare data. Many practices are also enhancing patient outreach efforts through coordination with specialized nursing services and ancillary caregivers. The best practice recommendations for safety monitoring described in this article, based on experiences in real-world clinical settings, may enable early detection and management of autoimmune events, and help with implementation of monitoring requirements while maximizing the benefits of alemtuzumab treatment for MS patients.
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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 | 0.019 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".