Comparative efficacy and safety between Micro-Percutaneous Nephrolithotomy (Micro-PCNL) and retrograde intrarenal surgery (RIRS) for the management of 10–20 mm kidney stones in children: A systematic review and meta-analysis
Bibliographic record
Abstract
Objectives: Kidney stone in children is a recurring problem that requires multiple interventions over time. Minimally-invasive approach, such as Extracorporeal Shockwave Lithotripsy (ESWL) is recommended for moderately-sized stones. However, since ESWL is associated with multiple interventions, Micro-Percutaneous Nephrolithotomy (Micro-PCNL) and Retrograde Intrarenal Surgery (RIRS) can also be considered to treat kidney stones in pediatric patients. Both approaches have their respective advantages and disadvantages. In this study, we aimed to compare the efficacy and safety of Micro-PCNL and RIRS in pediatric patients with kidney stones. Methods: This systematic review and meta-analysis adhered to the PRISMA guideline and Cochrane Handbook of intervention. The included studies were obtained from the PubMed and ScienceDirect databases. The protocol of this review has been registered in PROSPERO (CRD42021265894). The quality of the studies was assessed using the Newcastle-Ottawa Scale, outcomes were analyzed using STATA®16, and certainty of evidence was evaluated using GRADE. Results: A total of 239 participants were included in this study, divided into the Micro-PCNL (n = 112) and RIRS (n = 127) procedure groups. Statistical analysis revealed a significantly lower requirement of postoperative stenting procedure in Micro-PCNL compared to RIRS (OR 0.09; 95%CI 0.02, 0.47; p < 0.01). However, no significant difference was found in stone-free rate (p = 0.86), operative time (p = 0.09), UTI incidence (p = 0.67), blood transfusion requirement (p = 0.95), and length of stay (p = 0.77). Conclusion: Micro-PCNL is superior to RIRS in managing pediatric kidney stones,10-20 mm in size based on their comparable SFR and fewer requirements of additional stenting procedures.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.021 | 0.038 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".