Dusting versus Basketing during Ureteroscopy–Which Technique is More Efficacious? A Prospective Multicenter Trial from the EDGE Research Consortium
Bibliographic record
Abstract
PURPOSE: There is scant evidence in the literature to support dusting vs active basket extraction during ureteroscopy for kidney stones. We prospectively evaluated and followed patients to determine which modality produced a higher stone-free rate with the fewest complications. MATERIALS AND METHODS: Members of the Endourologic Disease Group for Excellence research consortium prospectively enrolled patients with a renal stone burden ranging from 5 to 20 mm in this study. A holmium laser was used and all patients were stented postoperatively. Ureteral access sheaths were used in 100% of basketing cases while sheaths were optional when dusting. The primary study outcome was the stone-free rate at 6 weeks as determined by x-ray and ultrasound. RESULTS: , p <0.001). The stone-free rate was significantly higher in the basketing group on univariate analysis (74.3% vs 58.2%, p = 0.04) but not on multivariate analysis (1.9 OR, 95% CI 0.9-4.3, p = 0.11). In patients who underwent a basketing procedure operative time was 37.7 minutes longer than in those treated with a dusting procedure (95% CI 23.8-51.7, p <0.001). There was no statistically significant difference in complication rates, hospital readmissions or additional procedures between the groups. CONCLUSIONS: The stone-free rate was higher for active basket retrieval of fragments at short-term followup on univariate analysis but not on multivariate analysis. There was no difference in postoperative complications or procedures. The 2 techniques should be in the armamentarium of the urologist.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".