Consensus-based Recommendations on the Management of Immunosuppression After Squamous Cell Carcinoma Diagnosis in Kidney Transplant Recipients: An International Delphi Consensus Statement
Bibliographic record
Abstract
Background: Posttransplant immunosuppression in kidney transplant recipients is associated with an increased risk of developing cutaneous squamous cell carcinoma (CSCC), contributing to significant morbidity and mortality. Various dermatological and immunosuppression modulation strategies have been identified that may reduce the risk of CSCC, both in primary and secondary prevention settings. Recent recommendations have provided consensus regarding dermatological approaches to prevent CSCC. Comparable transplant nephrology recommendations to guide immunosuppression modulation for CSCC prevention are currently lacking, leading to marked variation in practice. Methods: To address this knowledge gap, 46 international transplant nephrology experts participated in a 3-round Delphi survey to develop consensus recommendations for CSCC secondary prevention based on the actinic damage and skin cancer index stages of CSCC. Results: The panel of experts reached consensus to consider a change in immunosuppression after multiple low-risk invasive CSCC (stage 5a, 1/y >3 y) and encouraged collaboration with dermatology to optimize dermatologic preventative care after the first CSCC. There was also consensus to prioritize azathioprine modification where this is present in an immunosuppressive regimen. Conclusions: This study provides the first international consensus recommendations for management of immunosuppression in kidney transplant recipients at discrete stages of CSCC. Additional prospective studies are necessary to determine the optimal management of immunosuppression in this patient population. These recommendations have been endorsed by the Board of the American Society of Transplantation.
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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 | 0.237 | 0.270 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".