Percutaneous nephrolithotomy vs retrograde intrarenal surgery for renal stones: a Cochrane Review
Bibliographic record
Abstract
Objectives To assess the effects of percutaneous nephrolithotomy (PCNL) vs retrograde intrarenal surgery (RIRS) for the treatment of renal stones in adults. Methods We performed a comprehensive search of the Cochrane Library, MEDLINE, Embase, three other databases, trials registries, other sources of the grey literature, and conference proceedings up to 23 March 2023. We applied no restrictions on publication language or status. Screening, data extraction, risk‐of‐bias assessment, and certainty of evidence (CoE) rating using the Grading of Recommendations Assessment, Development and Evaluations (GRADE) approach were done in duplicate by two independent reviewers. This co‐publication focuses on the primary outcomes of this review only. Results We included 42 trials that met the inclusion criteria. Stone‐free rate (SFR): PCNL may improve SFRs (risk ratio [RR] 1.13, 95% confidence interval [CI] 1.08–1.18; I 2 = 71%; 39 studies, 4088 participants; low CoE). Major complications: PCNL probably has little to no effect on major complications (RR 0.86, 95% CI 0.59–1.25; I 2 = 15%; 34 studies, 3649; participants; moderate CoE) compared to RIRS. Need for secondary interventions: PCNL may reduce the need for secondary interventions (RR 0.31, 95% CI 0.17–0.55; I 2 = 61%; 21 studies, 2005 participants; low CoE) compared to RIRS. Conclusion Despite shortcomings in most studies that lowered our certainty in the estimates of effect to mostly very low or low, we found that PCNL may improve SFRs and reduce the need for secondary interventions while not impacting major complications. Ureteric stricture rates may be similar compared to RIRS. We expect the findings of this review to be helpful for shared decision‐making about management choices for individuals with renal stones.
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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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.010 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".