Impact of Open Vs Laparoscopic Kidney Harvesting and Routine Use of Double J Stent on Longterm Outcome of Ureteral Complications in Renal Transplants
Bibliographic record
Abstract
Background : Ureter related complications are an important cause of morbidity in renal transplant patients. Herein we present our experience and management of such complications. The impact of laproscopic live donor nephrectomy on ureter related complications was also analyzed. Methods: Our practice of vesico-ureteral anastomosis has evolved as phase I from 1989 to 1993 when stent was put only when felt necessary, phase II from January 1994 to April 1995, when stent placement was randomized and phase III from May 1995 to December 2010, where all anastomosis were stented. Kidney was removed by open method till 1998 and laparoscopic nephrectomy (left side) was started from 2002 onwards. Results : Incidence of ureteral leak without DJ stent was 6.1% and 0.4% with stent. Overall incidence of ureteral stenosis was 0.8%. Incidence of stenosis in stented and non-stented group was 0.8% and 2.3% respectively. Treatment offered for ureteral stenosis was percutaneous nephrostomy and antegrade stent. Ureteral leak was present in 0.3% and 1.5% in open and laproscopic method respectively and ureteral stenosis in 0.9% and 0.75% respectively. Conclusion : Two major ureter related complications were leak and stenosis and three-fourth of them resolved by minimal invasion. Major surgical revision was needed in around one-fourth of cases. Stent use helps in reducing incidence of ureteral leak, but doesn’t seem to have any impact on ureteral stenosis. Laparoscopic technique doesn’t seem to have any adverse effect on ureter-related complications. doi:10.4021/wjnu25w
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".