Flexible ureteroscopy for large renal stones: Are we pushing the limits: A multi-center retrospective analysis
Bibliographic record
Abstract
Background: Managing large renal stones presents challenges. While percutaneous nephrolithotripsy (PCNL) is the goldstandard, flexible ureteroscopy (f-URS) with laser technology has emerged as a minimally invasive alternative.Objective: To evaluate the outcomes of f-URS for renal stones larger than 20 mm in three centers and identify factorsthat influence stone-free rates (SFR).Patients and Methods: A retrospective analysis of 423 patients who underwent f-URS with holmium laser lithotripsyfor renal stones greater than or equal to 20 mm between January 2021 and October 2023 was conducted. Data fromthree centers in UAE, Canada, and Egypt were analyzed. Stone size, site, density, preoperative stenting, operative time,postoperative complications, and SFR at 30 days were assessed. Univariate and multivariate analyses were performed toidentify factors influencing SFR.Results: A total of 103 patients met the inclusion criteria. The median stone size was 25 mm. Preoperative stents wereinserted in around half of the cohort. Disposable f-URS and ureteral access sheath were used in the majority of patients.Median operative time was 94 min. Postoperative complications occurred in 21%. Significant residual fragments wereassessed 30 days postoperatively, around 50% of the study cohort required auxiliary procedures. Univariate analysisrevealed no significant associations between SFR and stone size, location, number, density, or preoperative stenting.Conclusion: In this multi-center study, f-URS with holmium laser lithotripsy could not achieve decent early stone-freerates for large renal stones, further research is needed to optimize treatment strategies for large 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".