Comparison of the Efficacy and Safety of Extracorporeal Shock Wave Lithotripsy and Flexible Ureteroscopy for Treatment of Urolithiasis in Horseshoe Kidney Patients: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Urolithiasis is the most common complication of horseshoe kidney (HK), which can be treated by extracorporeal shock wave lithotripsy (ESWL), flexible ureteroscopy (FURS), and percutaneous nephrolithotomy (PCNL). When comparing treatments of ESWL and FURS, it is unclear which is more efficient and safe. The objective of this study was to compare the efficacy and safety of FURS and SWL for the treatment of urolithiasis in HK patients. Methods: A systematic search of the Web of Science, PubMed, and EMBASE was performed in February 2021. Newcastle-Ottawa Scale (NOS) was used to assess the risk of bias in each study. Results: Five studies published between 2008 and 2018 were synthesized in the present meta-analysis. The study revealed that FURS compared with SWL had greater initial and overall stone-free rates (SFRs). Risk ratios (RRs) were 2.46 (P < 0.00001) in initial SFRs, 1.36 (P = 0.02) in overall SFRs. No differences were found in the retreatment ratio, RRs were 0.49 (P = 0.43). In addition, no major complications were encountered, and all the complications were mild to moderate. Conclusion: The study demonstrated that FURS and SWL are effective and safe treatments for patients with HK with stones (<20 mm). Moreover, FURS has greater clearance rates and lower complication rates than SWL.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.036 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".