Review: Transplanting kidneys from donors with small renal masses — a strategy to expand the donor pool
Bibliographic record
Abstract
INTRODUCTION: Renal transplantation is the optimal treatment for end-stage renal disease, but organ demand continues to outstrip supply. The transplantation of kidneys from donors with small renal masses (SRMs) represents a potential avenue to expand the donor pool. We reviewed all published cases of transplants from donors with SRMs and we present followup data, best practices, and outline an actionable series of steps to guide the implementation of such transplants at individual centers. METHODS: A detailed literature search of the MEDLINE/PubMed and SCOPUS databases was performed. Thirty unique data sets met inclusion criteria and described the transplantation of tumor-ectomized kidneys; nine data sets described the transplantation of contralateral kidneys from donors with SRMs. RESULTS: A total of 147 tumorectomized kidneys have been transplanted. Pathology revealed 120 to be renal cell carcinomas (RCCs), of which 116 were stage T1a (0.3-4 cm). The mean followup time was 44.2 months (1-200). A single suspected tumor recurrence occurred in one patient nine years post-transplantation and it was managed with active surveillance. Twenty-seven kidneys have been transplanted from deceased donors with contralateral renal masses. Pathology revealed 25 to be RCCs, of which 19 were confirmed to be stage T1 (<7 cm). The mean followup time was 46.7 months (0.5-155). One recipient developed an RCC and underwent curative allograft nephrectomy. CONCLUSIONS: Careful use of kidneys from donors with SRMs is feasible and safe, with an overall recurrence rate of less than 1.5%. The use of such kidneys could help alleviate the organ shortage crisis.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".