Incidence and risk factors for skin cancer in liver transplant recipients: A systematic review
Bibliographic record
Abstract
Skin cancer is a frequent complication of liver transplantation; however, contemporary incidence rates and associated risk factors remain poorly defined. We conducted a systematic review to summarize the literature on the incidence and risk factors of skin cancer after liver transplant. In July 2024, we searched MEDLINE, CINAHL, EMBASE, ClinicalTrials.gov, ProQuest Dissertations and Theses, and conference proceedings for studies on skin cancer following adult liver transplantations. Primary outcomes included incidence rates, number of de novo tumors, standardized incidence ratios, and significant risk factors for basal cell carcinoma, squamous cell carcinoma, melanoma, and skin cancer. Of the 522 unique studies generated by the search, 90 studies with 323,317 total liver transplant recipients were included. The mean incidence of skin cancer across studies was 1220.3 (SD=1400.0) cases per 100,000 person-years. Incidence rates and standardized incidence ratios varied substantially across studies but were consistently higher in liver transplant recipients than in the general population for non-melanoma skin cancers, most notably squamous cell carcinomas, but not for melanoma. The prominent risk factors included male sex, advanced age, fair complexion, alcohol consumption, sun exposure, and the use of cyclosporine rather than tacrolimus in immunosuppressive regimens. This study was limited by the heterogeneity of the study population, methods, and definitions of skin cancer. Overall, liver transplant recipients face heightened long-term risks of non-melanoma skin cancers, and while key risk factors are well established, there remains a gap in skin cancer surveillance and patient education within this population.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.010 | 0.013 |
| 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) | 0.004 | 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".