Clinical Implications of Skin Cancer in Kidney Transplant Recipients in the Era of Immune Checkpoint Inhibitors
Bibliographic record
Abstract
Long-term survival has improved in kidney transplant recipients (KTRs) due to effective surgical techniques and anti-rejection therapies. Chronic immunosuppression associated with it has led to several types of skin cancers leading to substantial morbidity and mortality. Structured patient education including sun protective behaviors, regular dermatological surveillance, nicotinamide, long-chain omega-3 polyunsaturated fatty acids (PUFAs), early switch to mammalian target of rapamycin inhibitors (mTORis), combining them with low-dose calcineurin inhibitors (CNIs), can decrease the cancer risk. Checkpoint inhibitors (CPIs) are the major backbone of the treatment of advanced skin cancers. Unfortunately, these agents can increase the risk of graft rejection. Prospective studies done so far looking at combining steroids with CPI in treatment of skin cancer in KTRs have shown mixed results. Adoption of the weight-based approach of CPI has shown to decrease the amount of drug exposure with acceptable outcomes in the general population, which is something that can be studied in KTRs with skin cancer. Also, it is reasonable to consider surveillance allograft biopsies in KTRs receiving CPIs to detect early subclinical rejection. More studies are needed to develop guidelines to safely treat this population with minimal graft rejection. We conducted a comprehensive literature review from PubMed on skin cancer in kidney transplant patients, focusing on incidence, risk factors, protective behaviors, financial and treatment implications, especially with regards to CPIs therapy. We also discussed potential newer treatment options that will decrease skin cancer risk, as well as graft rejection.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".