Trends in Renal Stone Clearance after Ureteroscopy: A Review
Bibliographic record
Abstract
Background and Objectives Stone clearance rate in ureteroscopy has varied over the years. This study aims to review the stone clear-ance rate over the last 25 years and assess the change over time. We have analyzed the reasons for the peaks and troughs in stone clearance rate to see if it correlates with any factors such as the introduction of new technology like the holmium laser, flexible ureteroscopy, access sheaths, and digital ureteroscopy. Material and Methods We performed a PubMed search (August 2019) for papers including the terms “lithiasis”, “stone clear-ance”, “calculi”, “kidney stone”, “ureteric stone”, “ureteroscopy”, “holmium laser”, “retrorenal surgery” in their title and published between the years 1994 and 2019. The stone size, stone clearance rate and mode of imaging to determine clearance rates were recorded. For data analysis, only prospective studies with a minimum of 50 patients and ureteroscopy arm of prospective randomized controlled trials were included. Results We reviewed 16 papers with a total of 1,689 patients with renal stones. Average stone clearance was 80% and the median stone size was 11.0mm. Stone clearance was determined by either: Computed tomography (CT) scan (8 studies), x-ray alone (3 studies), x-ray and ultrasound (3 studies) or not mentioned (2 studies). CT scan yielded lower stone clearance rates than x-ray due to the increased detail shown on CT. For studies that used absolute clearance with no residual stones, average clearance was 52%, and this stone clearance rate increased as the cut-off size used to determine the stone-free rate was increased. Conclusion This study highlights that stone clearance rate after ureteroscopy varies significantly amongst different pa-pers because of the stone size used to define ‘stone-free rate’ and the method of imaging used to determine stone clearance. The study also shows that stone clearance rates have not improved significantly over time, despite the introduction of advances in technology.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.018 | 0.020 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".