Contemporary risk factors for ureteral stricture following renal transplantation
Bibliographic record
Abstract
INTRODUCTION: Allograft ureteral strictures after renal transplantation impact graft function and increase patient morbidity. They can be challenging to treat and may require complex surgical repair. Therefore, the objective of this study was to identify contemporary risk factors for the development of post-renal transplant ureteral strictures. METHODS: A retrospective analysis was performed on all renal transplant patients at Vancouver General Hospital from 2008-2019. Demographics, clinical parameters, and outcomes were compared between patients who did and did not develop ureteral strictures. Putative risk factors for ureteral stricture were analyzed using logistic regression. RESULTS: A total of 1167 patients were included with a mean followup of 61.9±40.8 months. Ureteral strictures occurred in 25 patients (2.1%). Stricture patients had no demographic differences compared to non-stricture patients but had significantly higher rates of postoperative complications, longer hospital stays, and decreased renal function one year post-transplant (all p<0.05). On multivariable analysis, cold ischemia time >435 minutes (odds ratio [OR] 43.9, confidence interval [CI] 1.6-1238.8, p=0.027), acute rejection (OR 3.0, CI 1.1-7.4, p=0.027), and postoperative complications (OR 112.4, CI 2.4-5332.6, p=0.016) were risk factors for stricture. CONCLUSIONS: Renal transplant patients with ureteral stricture experience greater morbidity and reduced post-transplant renal function compared to non-stricture patients. Our findings support attempts to reduce cold ischemia time, acute rejection, and postoperative complications to mitigate this potential complication. Our study is limited by the low incidence of ureteral stricture resulting in a small sample of stricture patients. Future research in a larger, multicenter setting is warranted.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".