Clinical efficacy of percutaneous nephrolithotomy versus retrograde intrarenal surgery for pediatric kidney urolithiasis
Bibliographic record
Abstract
BACKGROUND: Percutaneous nephrolithotomy (PCNL) and retrograde intrarenal surgery (RIRS) are widely used for pediatric upper tract stones; however, comparisons of their clinical efficacies are needed. METHODS: Literature searches for relevant articles were performed using PubMed, the Cochrane Central Register of Controlled Trials, Embase and the China CNKI database. Study quality was assessed by Jadad and Newcastle-Ottawa Scales. Standard mean differences (SMDs) or odds ratios (OR), and 95% confidential intervals (95% CIs) were pooled for meta-analysis. In addition, data was evaluated the quality of the body of evidence by means of grading of recommendations assessment, development, and evaluation (GRADE). RESULTS: Data from 4 studies (231 PCNL, 212 RIRS cases) were analyzed. There was no significant difference in operation time (SMD: 1.39; 95% CIs: -0.049 to 2.82; P = .058), overall stone-free rate (OR: 3.72; 95% CIs: 0.55-25.22; P = .18), or complication rate (OR: 1.92; 95% CIs: 0.90-4.07; P = .091). PCNL cases had longer hospital stays (SMD: 1.22; 95% CIs: 0.95-1.50; P < .001), but showed a higher stone-free rate for stones greater than 20 mm (OR: 6.38; 95% CIs: 1.83-22.22; P = .004). For stones less than 20 mm, however, no significant difference between PCNL and RIRS was found (OR: 0.92; 95% CIs: 0.33-2.55; P = .87). The quality of evidence based on the GRADE system was low. CONCLUSION: Results of our systematic review and meta-analysis suggest that, for the treatment of larger kidney stones (>20 mm) in pediatric patients, PCNL is a better option due to its higher stone-free rate, although RIRS may be associated with shorter hospital stays. A large-scale clinical trial is necessary to validate our findings.
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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 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".