Optimal management of distal ureteric strictures following renal transplantation: a systematic review
Bibliographic record
Abstract
Our objective was to define optimal management of distal ureteric strictures following renal transplantation. A systematic review on PubMed identified 34 articles (385 patients). Primary endpoints were success rates and complications of specific primary and secondary treatments (following failure of primary treatment). Among primary treatments (n = 303), the open approach had 85.4% success (95% CI 72.5-93.1) and the endourological approach had 64.3% success (95% CI 58.3-69.9). Among secondary treatments (n = 82), the open approach had 93.1% success (95% CI 77.0-99.2) and the endourological approach had 75.5% success (95% CI 62.3-85.2). The most common primary open treatment was ureteric reimplantation (n = 33, 81.8% success, 95% CI 65.2-91.8). The most common primary endourological treatment was dilation (n = 133, 58.6% success, 95% CI 50.1-66.7). Fourteen complications, including death (4 weeks post-op) and graft loss (12 days post-op), followed endourological treatment. One complication followed open treatment. This is the first systematic review to examine the success rates and complications of specific treatments for distal ureteric strictures following renal transplantation. Our review indicates that open management has higher success rates and fewer complications than endourological management as a primary and secondary treatment for post-transplant distal ureteric strictures. We also outline a post-transplant ureteric stricture evaluation and treatment algorithm.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".