Approaches to discontinuing efalizumab: an open-label study of therapies for managing inflammatory recurrence
Bibliographic record
Abstract
BACKGROUND: Efalizumab is a humanized recombinant monoclonal IgG1 antibody for the treatment of moderate-to-severe plaque psoriasis. When treatment discontinuation is necessary, however, some patients may experience inflammatory recurrence of the disease, which can progress to rebound if untreated. This analysis evaluated approaches for managing inflammatory recurrence after discontinuation of efalizumab. METHODS: An open-label, multicentre, investigational study was performed in 41 patients with moderate-to-severe plaque psoriasis who had recently completed clinical studies with efalizumab and had developed signs of inflammatory recurrence following abrupt cessation of treatment. Patients were assigned by the attending physicians to receive one of five standardised alternative systemic psoriasis treatment regimens for 12 weeks. Efficacy of the different therapy options was assessed using the physician's global assessment (PGA) of change over time. RESULTS: More favourable PGA responses were observed in patients changing to cyclosporin (PGA of 'good', 'excellent' or 'cleared': 7/10 patients, 70.0%) or methotrexate (9/20, 45.0%), compared with those receiving systemic corticosteroids (2/8, 25.0%), retinoids (0/1, 0.0%) or combined corticosteroids plus methotrexate (0/2, 0.0%). While the majority (77.8%) of patients showed inflammatory morphology at baseline, following 12 weeks of the alternative therapies the overall prevalence of inflammatory disease was decreased to 19.2%. CONCLUSION: Inflammatory recurrence after discontinuation of efalizumab therapy is a manageable event, with a number of therapies and approaches available to physicians, including short courses of cyclosporin or methotrexate.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".