Robotic Flexible Ureteroscopy: Systematic Review and Meta-Analysis of Surgical Efficacy, Safety and Ergonomic Outcomes
Bibliographic record
Abstract
Robotic flexible ureteroscopy (RFURS) has emerged as a novel approach to address the ergonomic challenges and technical limitations of conventional flexible ureteroscopy (FURS) for renal stone management. While FURS remains a cornerstone in treating nephrolithiasis, prolonged procedures contribute to surgeon fatigue, musculoskeletal strain, and increased radiation exposure. Despite growing adoption, the literature lacks a synthesis of the clinical benefits, cost-effectiveness, and long-term outcomes of RFURS compared to conventional approaches. The objective of our study is to synthesize the existing evidence in the literature and produce a comprehensive systematic review of RFURS. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we searched PubMed, Embase, and Cochrane (inception: June 2025) for clinical studies on RFURS. Meta-analysis used random-effects models for pooled estimates of stone-free rates (SFRs), operative times and complications. The risk of bias was assessed by the Newcastle-Ottawa Scale and the Cochrane risk tool. Twelve studies (706 patients) were included. RFURS achieved a pooled SFR of 87.4% (95% confidence interval (CI): 82.7-92.0%), comparable to conventional FURS. Pooled operative time was 94.7 minutes (95% CI: 78.9-110.5), longer than conventional FURS. Complication rates were 10.6% (95% CI: 5.1-16.1%) similar to conventional FURS. Ergonomics were superior, with reduced surgeon fatigue and radiation exposure. Learning curves vary according to the robot platforms and early proficiency is noted among experienced endoscopists. Cost-effectiveness data were limited. RFURS demonstrates non-inferior efficacy and safety to conventional FURS, with enhanced ergonomics and manageable learning curves. High heterogeneity and limited cost data necessitate larger comparative studies.
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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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.021 | 0.035 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".