Risk of Developing Melanoma With Systemic Agents Used to Treat Psoriasis: A Review of the Literature
Bibliographic record
Abstract
BACKGROUND: Psoriasis is a chronic inflammatory skin disease induced by autoimmune-like dysregulation of the immune system. Treatment options have drastically evolved in recent years, and treatment advances that target specific cytokines and other molecules involved in dysregulation have had a profound effect in controlling the disease. OBJECTIVE: We reviewed the literature to assess the risk of developing melanoma with conventional therapies and newer agents used to treat psoriasis. METHODS: A comprehensive literature search using Medline (via Ovid) and Embase was conducted. RESULTS: The majority of studies reviewed reported insignificant results. Potential risk for melanoma was identified for only 3 out of 15 anti-psoriatic treatments analyzed: adalimumab (relative risk 1.8, 95% CI 1.06-3.00), etanercept (relative risk 2.35, 95% CI 1.46-3.77) and infliximab (Empirical Bayes Geometric Mean 7.90, 95% CI 7.13-8.60). The confidence intervals provided are from prior studies. There are not enough collective data on newer agents to make any conclusions on risk. CONCLUSIONS: We were unable to identify any substantial risk for developing melanoma due to the use of anti-psoriatic treatments. Until additional long-term registry data become available, it would be prudent to continue screening patients with psoriasis at baseline and periodically for melanoma when these agents are used.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".