Impact of Preoperative Stenting on the Outcome of Flexible Ureterorenoscopy for Upper Urinary Tract Urolithiasis: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
PURPOSE: This study aimed to investigate the effect of preoperative stenting (POS) on the perioperative outcomes of flexible ureterorenoscopy (fURS). MATERIALS AND METHODS: A systematic review and meta-analysis was conducted based on the PRISMA statement. From the initially retrieved 609 reports, we excluded the ineligible studies at 2 stages. We only included studies that contained fURS patients with and without POS in the same report. Data of patients who underwent semirigid ureteroscope only were excluded from the analysis. The Newcastle-Ottawa Scale (NOS) system was applied for risk of bias assessment. RESULTS: A total of 20 studies including 5,852 patients were involved. 48.5% of the patients had preoperative stent. Stone-free rate was significantly higher with prestenting; odds ratio (OR) was 1.98 (95% CI: 1.51-2.58) (Z = 5.02; p = 0.00001). It also displayed tendency toward lower complications; OR was 0.74 (95% CI: 0.52-1.05) (Z = 1.67; p = 0.09). POS significantly increased the use of ureteral access sheath; OR was 1.49 (95% CI: 1.05-2.13) (Z = 2.22; p = 0.03). Risk of bias assessment showed 13 and 7 studies with low and moderate risk, respectively. CONCLUSIONS: POS clearly improves the stone-free rates after fURS. It might reduce the complications, especially ureteral injury. These findings might help solve the current debate and can be useful for urologists during patient counselling for a proper decision-making.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.038 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".