Pharmacologic Immunomodulation and Cutaneous Malignancy in Rheumatoid Arthritis, Psoriasis, and Psoriatic Arthritis
Bibliographic record
Abstract
OBJECTIVE: It is unclear if skin cancer risk is affected by the use of immunomodulatory medications in rheumatoid arthritis (RA), psoriasis, and psoriatic arthritis (PsA). The purpose of this study is to evaluate and summarize the available data pertinent to this question. METHODS: The English language literature on PubMed was searched with a combination of phrases, including "malignancy," "skin cancer," "squamous cell carcinoma," "basal cell carcinoma," "melanoma," "psoriasis," "psoriatic arthritis," and "rheumatoid arthritis" in addition to the generic names of a variety of common immunomodulatory drugs. Relevant articles were identified and data were extracted. RESULTS: In total, 2218 potentially relevant articles were identified through the search process. After further screening, 20 articles relevant to RA were included. An additional 19 articles relevant to either psoriasis or PsA were included as well. RA may be a risk factor for the development of cutaneous malignancy. Treatment with tumor necrosis factor inhibitors increases the rates of non-melanoma skin cancer (NMSC) in RA and psoriasis. This risk doubles when combination methotrexate therapy is used in RA. Methotrexate may increase the risk of malignant melanoma in patients with RA and the risk of NMSC in psoriasis. Cyclosporine and prior phototherapy significantly increase the risk of NMSC. CONCLUSION: RA may potentiate the risk of cutaneous malignancy and therefore dermatologic screening in this population should be considered. The use of immunomodulatory therapy in RA, psoriasis, and PsA may further increase the risk of cutaneous malignancy and therefore dermatologic screening examinations are warranted in these groups. More careful recording of skin cancer development during clinical trials and cohort studies is necessary to further delineate the risks of immunomodulatory therapy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 teacher head, 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".