Exploring Skin Cancer Risk in Chronic Kidney Disease Patients: A Single Arm of Meta-analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Skin cancers are among the most prevalent malignancies that develop following renal transplantation. This review aims to provide a comprehensive and up-to-date overview of the risk of skin cancer among patients with chronic kidney disease. A systematic review and meta-analysis were conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We searched Scopus, PubMed, Embase, and Komaki databases for research publications on chronic kidney disease and skin cancer published between February 2016 and January 2023. The prevalence of skin cancer among chronic kidney disease patients was meta-analyzed. A random-effects meta-regression was performed, and the risk of bias was assessed using the Newcastle–Ottawa Scale. A total of 16 studies, encompassing 151,987 patients, fulfilled the inclusion criteria for this systematic review. The aggregated incidence of non-melanoma skin cancer among renal transplant recipients was 4.32% (95% CI, 4.1–4.5%), while the incidence of melanoma skin cancer was 1.92% (95% CI, 1.85–1.99%). The pooled prevalence of non-melanoma skin cancer and melanoma skin cancer was 5.7% (95% CI, 1.1–10.3%) and 0.25% (95% CI, 0.11–0.39%), respectively. In conclusion, our study confirms a heightened risk of skin cancer in chronic kidney disease patients. Further research with larger samples and enhanced surveillance is crucial to better understand and address this risk.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it