Percutaneous Nephrostomy Versus Retrograde Ureteral Stent for Management of Malignant Ureteral Obstruction in Adults: a Systematic Review of the Literature
Bibliographic record
Abstract
BackgroundMalignant ureteral obstruction (MUO) is a common presentation in advanced urological and non-urological malignancies. Percutaneous nephrostomy (PCN) and retrograde ureteral stent (RUS) are the most commonly performed procedures to relieve the obstruction. The comparative effectiveness of PCN and RUS for decompression of MUO remains uncertain.PurposeTo systematically review the literature for evidence of improved efficacy of one of these procedures in terms of renal function preservation and clinical outcomes.MethodsWe searched Ovid Medline, Ovid EMBASE, CINAHL, Cochrane Central Register of Controlled Trials (CENTRAL), and Scopus from the date of inception to October 2022. In addition, gray literature was searched through OpenGray (https://opengrey.eu/), dissertation and thesis database (ProQuest) via (https://www.proquest.com), and Clinical trial.gov website. The reference lists of all the included studies were also searched.Two reviewers independently reviewed and selected studies, assessed the quality, and extracted the data.ResultsOverall, 25 eligible studies including 1864 patients compared PCN and RUS (head-to-head). PCN and RUS were found to be similarly effective in improving renal function. However, PCN appears to be superior in maintaining this reduction. The complication rate and quality of life were comparable between the 2 methods, but the length of hospital stay and the financial cost were significantly higher in the PCN group. The mean technical success rate in RUS was 70.3% (21% to 100%) and in PCN was 98.8% (90% to 100%). The conversion rate from RUS to PCN ranged from 10% to 42.6% (mean = 22.5%), while internalization of the PCN occurred in 11.7% to 98% of the patients (mean = 45.5%).ConclusionsBoth diversional methods are effective in management of MUO. However, because of the heterogeneity of the included studies, the superiority of one of the procedures cannot be concluded.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".