The Clinical Efficacy of Percutaneous Nephrolithotomy and Flexible Ureteroscopic Lithotripsy in the Treatment of Calyceal Diverticulum Stones: A Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: The clinical efficacy of percutaneous nephrolithotomy (PCNL) and flexible ureteroscopic lithotripsy (FURL) in the treatment of calyceal diverticulum stones (CDs) remains controversial. We performed a meta-analysis to assess the clinical efficacy of PCNL and FURL in the treatment of CDs. METHODS: We searched a number of relevant electronic databases including China National Knowledge Infrastructure (CNKI), MEDLINE, PubMed, Web of Science, EMBASE, and Cochrane Library until January 31, 2022. STATA 15.1 software was used to analyze all data for this article. The quality of these studies was assessed by the Newcastle-Ottawa Scale (ranged from 0 to 9 stars). RESULTS: Finally, we selected 11 high-quality studies in our meta-analysis,which containing 486 patients. Meta-analysis showed that PCNL had higher stone-free rate [OR=3.55, 95% CI: 2.07 -6.10, P = 0.000], symptom-free rate [OR=3.56, 95% CI: 1.51 -8.38, p= 0.004], while it was slightly inferior to the FURL in bleeding volume [SMD = 1.27, 95% CI: (0.67,1.87), P = 0.000], hospital stay [SMD =2.86, 95% CI: 1.75-3.97, P = 0.000] and complication rate [OR =1.92, 95% CI: 1.10-3.33, P = 0.021], and there was no significant difference in operative time [SMD = -0.011, 95% CI: (-0.41,0.39), P = 0.957]. CONCLUSION: PCNL is safe and effective in the treatment of CDs, which can be considered as the first choice for the clinical treatment of CDs.
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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 | 0.018 | 0.027 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.073 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| 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".