Successful use of cemiplimab in a high immunologic risk kidney transplant recipient with metastatic squamous cell carcinoma
Bibliographic record
Abstract
The widespread use of immunotherapy in the management of cancers has led to improved overall survival and progression free survival. Due to increased risk of allograft rejection, organ transplant recipients are often excluded in clinical trials or offered immunotherapy only as salvage therapy. We report a case of successful use of Cemiplimab, an immune checkpoint inhibitor, in a high immunologic risk kidney transplant recipient who was diagnosed with metastatic squamous cell carcinoma (SCC) twenty-five months post-transplant. He started Cemiplimab five months post diagnosis of SCC, as third line therapy, after demonstrating progression of metastatic skull-based disease on prior lines of therapy. His maintenance immunosuppression was changed from triple immunosuppression with tacrolimus, mycophenolate and prednisone to sirolimus with a high trough target of 10-15 ng/mL and steroid therapy. He tolerated the high sirolimus trough and continued on Cemiplimab for six months with clinically stable allograft function and a good quality of life. Notably, he demonstrated response of his previously chemotherapy refractory metastatic disease. He passed away from radiation necrosis of the brain at sixty-eight months post-transplant.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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 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".