Biologic Therapy in Psoriasis: Perspectives on Associated Risks and Patient Management
Bibliographic record
Abstract
BACKGROUND: Previous publications have described practical considerations for initiating biologic therapy in psoriasis patients. However, most publications have focused on anti-tumor necrosis factor (TNF) therapy. OBJECTIVE: To create an evidence-based, practical tool that provides guidance on patient management for all biologics currently approved in Canada and the United States. METHODS: Psoriasis publications regarding safety issues in the initiation or monitoring of adalimumab, alefacept, etanercept, infliximab, or ustekinumab therapy were identified through a PubMed search. Phase III trials and open-label extensions (regardless of indication) and relevant guidelines from Health Canada were used to compile this review. RESULTS: Although these biologic agents have demonstrated efficacy in patients with psoriasis and are generally considered safe and well tolerated, rare but serious safety issues (ie, demyelination, infection, tuberculosis, malignancy, lymphoma, cardiovascular outcomes, hepatitis, pregnancy, surgery, and vaccination) have been observed. Attention to specific aspects of patient management (ie, prescreening requirements, symptoms to watch for, appropriate treatment, and referrals) is required to mitigate risk. CONCLUSION: Much of the evidence regarding the long-term safety of these agents has been based on experience in other patient populations. However, it does serve to guide us in understanding the risks that may impact the management of 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".