Systematic Review of Calcineurin Inhibitors and Incidence of Skin Malignancies after Kidney Transplantation in Adult Patients: A Study of 309,551 Cases
Bibliographic record
Abstract
The purpose of this systematic review and meta-analysis was to compare the risk of non-melanoma skin cancer (NMSC) and melanoma development in renal transplant recipients who receive calcineurin inhibitors to that of patients treated with other immunosuppressive agents, and investigate the possible association between the type of maintenance immunosuppression and the incidence of NSMC and melanoma in this group of patients. The authors searched databases such as PubMed, Scopus, and Web of Science for articles that would help establish the influence of calcineurin inhibitors on skin cancer development. The inclusion criteria for the study consisted of randomized clinical trials, cohort studies, and case-control studies that compared patients who received kidney transplants and were treated with a calcineurin inhibitor (CNI), such as cyclosporine A (CsA) or tacrolimus (Tac), to those who received alternative immunosuppressants and did not receive a CNI. Seven articles were analyzed overall. The results revealed a correlation between CNI treatment in renal transplant recipients and increased total skin cancer risk (OR 1.28; 95% CI: 0.10–16.28; p < 0.01), melanoma risk (OR 1.09; 95% CI: 0.25–4.74; p < 0.01), and NMSC risk (OR 1.16; 95% CI: 0.41–3.26; p < 0.01). In conclusion, the calcineurin inhibitors used after kidney transplantation are associated with a higher risk of skin cancer—both non-melanoma and melanoma—when compared with other immunosuppressive therapies. This finding suggests that careful monitoring for skin lesions in post-transplant patients must be conducted. However, the decision on the kind of immunotherapy used should always be considered on an individual basis for each renal transplant recipient.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".