Expanding living kidney donor criteria with ex-vivo surgery for renal anomalies
Bibliographic record
Abstract
INTRODUCTION: Renal transplantation remains the gold standard treatment for end-stage renal disease, with living donor kidneys providing the best outcomes in terms of allograft survival. As the number of patients on the waitlist continues to grow, solutions to expand the donor pool are ongoing. A paradigm shift in the eligibility of donors with renal anomalies has been looked at as a potential source to expand the living donor pool. We sought to determine how many patients presented with anatomic renal anomalies at our transplant centre and describe the ex-vivo surgical techniques used to render these kidneys suitable for transplantation. METHODS: A retrospective review was performed of all patients referred for surgical suitability to undergo laparoscopic donor nephrectomy between January 2011 and January 2015. Patient charts were analyzed for demographic information, perioperative variables, urological histories, and postoperative outcomes. RESULTS: 96 referrals were identified, of which 81 patients underwent laparoscopic donor nephrectomy. Of these patients, 11 (13.6%) were identified as having a renal anomaly that could potentially exclude them from the donation process. These anomalies included five patients with unilateral nephrolithiasis, four patients with large renal cysts (>4 cm diameter), one patient with an angiomyolipoma (AML) and one patient with a calyceal diverticulum filled with stones. A description of the ex-vivo surgical techniques used to correct these renal anomalies is provided. CONCLUSIONS: We have shown here that ex-vivo surgical techniques can safely and effectively help correct some of these renal anomalies to render these kidneys transplantable, helping to expand the living donor pool.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".