Alemtuzumab as Remission Induction Therapy in Behçet Disease: A 20-year Experience
Bibliographic record
Abstract
OBJECTIVE: To study the efficacy and safety of anti-CD52 antibody (alemtuzumab) in the treatment of refractory and relapsing Behçet disease (BD). METHODS: Thirty-two patients (22 women) with BD received 60 courses of alemtuzumab between 1994 and 2013. Three-dose regimens were used: 134 mg in 21 courses (Group 1), 95 mg in 18 courses (Group 2), and 60 mg in 21 courses (Group 3). Immunosuppressive drugs were stopped at the time of alemtuzumab, and prednisolone was reduced according to clinical response. Treatment response was assessed by clinical status, inflammatory activity, prednisolone dose, and the need for subsequent immunosuppressive drugs and disease relapse. RESULTS: After the first alemtuzumab course, 27 of 32 patients (84%) achieved partial or complete remission (CR). Fifty of 60 courses (83%) resulted in remission (66% CR) without differences in remission rates between dosing regimens. Profound lymphocyte depletion occurred after all courses. Relapse-free survival rates were 83.6% at 6 months and 52.8% at 12 months, and were higher among Group 1 patients (Group 1: 100% and 77.8%, Group 2: 81.3% and 37.5%, and Group 3: 65.0% and 37.1%, p < 0.001). Mild to moderate infusion reactions occurred after 16 courses (27%). Eight patients (25%) developed symptomatic thyroid disease. CONCLUSION: Alemtuzumab led to remission in the majority of patients with difficult-to-treat BD. Relapse was common and may be associated with lower dosing. Adverse events included infusion reactions and new autoimmunity. Achieving complete lymphocyte depletion did not affect the remission rate or duration.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".