Ureteral strictures post-kidney transplantation: Trends, impact on patient outcomes, and clinical management
Bibliographic record
Abstract
INTRODUCTION: Ureteral strictures post-kidney transplantation (KT) can be a significant morbidity to the patient, often requiring surgical intervention and impacting graft function. We sought to investigate the incidence, clinical management, and outcomes of ureteral strictures among kidney transplant recipients (KTRs) at a large, multiorgan transplant center. METHODS: We conducted a single-center cohort study looking at KTRs who had transplant surgery from January 1, 2005 to March 31, 2017 with at least one-year followup (n=1742). Any KTRs done outside of our center or simultaneous multiorgan transplants were excluded. The Kaplan-Meier product-limit method was used to determine the incidence of ureteral strictures. Risk factors for ureteric strictures and clinical outcomes among patients with vs. without ureteric strictures were analyzed using Cox proportional hazards models. RESULTS: The incidence of ureteral strictures was 1.31 (95% confidence interval [CI] 0.85, 2.01) per 100 person-years or a cumulative incidence of 1.2%. We did not find any donor or recipient demographic variables that were independently associated with an increased risk of ureteral stricture development. A large proportion was managed successfully with radiological intervention alone (47.6%). Ureteral strictures were associated with death-censored graft failure (hazard ratio [HR] 7.17, 95% CI 2.81, 18.30), total graft failure (HR 3.04, 95% CI 1.41, 6.59), and hospital re-admission (HR 2.52, 95% CI 1.58, 4.00). CONCLUSIONS: Although uncommon, ureteral strictures can significantly impact patient outcomes after KT. A better understanding of risk factors and clinical management will be important to ensure optimal graft outcomes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".