Monitoring and management of autoimmunity in multiple sclerosis patients treated with alemtuzumab: practical recommendations
Bibliographic record
Abstract
Alemtuzumab is a humanized anti-CD52 monoclonal antibody approved in more than 65 countries for the treatment of relapsing-remitting multiple sclerosis (RRMS). Compared with subcutaneous interferon-beta-1a, alemtuzumab significantly reduced clinical disease activity and the rate of brain volume loss, and improved disability outcomes in patients with active RRMS who were either treatment naive (CARE-MS I study) or who had an inadequate response (≥ 1 relapse after ≥ 6 months of treatment) to prior therapy (CARE-MS II study). Adverse events (AEs) associated with alemtuzumab include infusion-associated reactions, infections, and autoimmunity. The most commonly reported autoimmune AEs observed with alemtuzumab involve the thyroid gland, followed by immune thrombocytopenia and nephropathies. A monitoring program was designed and implemented to facilitate the early detection of autoimmune events to ensure timely and adequate management. The aim of this article is to provide physicians (including neurologists, general practitioners, endocrinologists, hematologists, and nephrologists who may be less familiar with the symptoms and treatment of autoimmune events), with practical real-world recommendations for the monitoring and management of autoimmunity associated with alemtuzumab treatment.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".