Percutaneous nephroscopy versus flexible ureteroscopy in the treatment of calyceal diverticulum calculi: a meta-analysis
Bibliographic record
Abstract
Abstract Background There is still controversy about the best minimally invasive surgical method for the treatment of calyceal diverticulum calculi. We conducted meta-analysis to evaluate the effectiveness and safety of PCNL and FURL in the treatment of calyceal diverticulum calculi. Methods We searched Pubmed, Cochrane Library, Web of Science, Embase, Clinical trial platform, CNKI, VIP until April 2024. We utilized the Newcastle–Ottawa Scale (NOS, 0 to 9 stars) to assess the quality of the included literature. Results Totally 15 high-quality studies with 755 patients were included in the meta-analysis. Meta-analysis showed that FURL group was better than PCNL group in blood loss [SMD = 1.713, 95%CI:(0.858, 2.568), Z = 3.928, P = 0.000] and hospital stay [SMD = 2.611, 95%CI: (1.726, 3.496), Z = 5.784, P = 0.000], there was no significant difference in operating time [SMD = 0.079, 95%CI:(-0.43, 0.589), Z = 0.306, P = 0.760], complication rate [OR = 1.793,95%CI: (0.952,2.602), Z = 1.586, P = 0.113], stone-free rate [OR = 1.339, 95%CI: (0.576, 3.112), Z = 0.678, P = 0.497] and symptom-free rate [OR = 3.826,95%CI: (0.561,10.238), Z = 0.966, P = 0.334] as well. Conclusion Whether FURL is indeed superior to PCNL in safety, whether FURL's efficacy is really close to PCNL, and whether FURL can surpass PCNL as the first choice for the treatment of renal diverticulum stones in the future need to be further verified by multi-center, large-sample and high-quality 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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.022 | 0.056 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| 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".