A Review of Malignancies Observed during Efalizumab (Raptiva®) Clinical Trials for Plaque Psoriasis
Bibliographic record
Abstract
BACKGROUND: Psoriasis is a chronic, incurable immune-mediated disease. Most therapies used for moderate to severe psoriasis are immunosuppressive. Agents that depress immune function, including traditional psoriasis therapies, have been associated with an increased incidence of malignancies. Efalizumab is a recombinant monoclonal immunoglobulin G1 (IgG1) antibody approved for use in psoriasis patients. OBJECTIVES: To evaluate the incidence of malignancy in patients receiving efalizumab during clinical trials compared with placebo-treated patients, psoriasis patients from external cohorts and the general US population. METHODS: Patient data were pooled from multiple phase III placebo-controlled, open-label efalizumab clinical trials, and the incidence rate of reported malignancies was calculated as a function of patient years of observation. The results for the efalizumab-treated patients were compared with the data on psoriasis patients from insurance claims databases and a registry of events in the general population. RESULTS: The efalizumab- and placebo-treated patients had similar incidence rates of malignancy, including lymphoproliferative disease, solid tumor, malignant melanoma and nonmelanoma skin cancer. The incidence of nonmelanoma skin cancers, including basal cell carcinoma and squamous cell carcinoma, in patients receiving efalizumab or placebo was elevated relative to the external databases. CONCLUSIONS: These results suggest that efalizumab treatment does not increase a patient's risk for malignancy. The difference observed with nonmelanoma skin cancer may be due to biases introduced by the clinical trial methodology. Additional patient observation is necessary to ascertain whether a link exists between efalizumab therapy and nonmelanoma skin cancer above that normally observed in psoriasis 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 | 0.013 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".