Ultrasound-Only Percutaneous Nephrolithotomy Is Safe and Effective Compared to Fluoroscopy-Directed Percutaneous Nephrolithotomy
Bibliographic record
Abstract
Introduction: Outcomes after ultrasound-only percutaneous nephrolithotomy (PCNL), in which no fluoroscopy is used, are not well known. The goal of this study was to compare outcomes of ultrasound-only and fluoroscopy-directed PCNL. Materials and Methods: Prospectively collected data from the Registry for Stones of the Kidney and Ureter database were reviewed for all patients who underwent PCNL at one academic center from 2015 to 2021. Primary outcomes were complications and stone-free rates (no residual fragments ≥3 mm). Results: Of the 141 patients who underwent ultrasound-only PCNL and 147 who underwent fluoroscopy-directed PCNL, there was no difference in complication rates (15% vs 16%, p = 0.87) or stone-free status (71% vs 65%, p = 0.72), respectively. After adjusting for body mass index, American Society of Anesthesiologists (ASA), stone size, and stone complexity by Guy score, ultrasound-only PCNL was not associated with any increased odds of complications (odds ratio [OR] 0.7, 95% confidence interval [CI] 0.3–1.6, p = 0.41) or residual stone fragments ≥3 mm (OR 1.0, 95% CI 0.5–1.9, p = 0.972) compared with fluoroscopy-directed PCNL. Ultrasound-only PCNL was associated with shorter operative time (median 99.5 vs 126 minutes, p < 0.001), and the use of ultrasound remained a significant predictor of short operative time (<100 minutes) after controlling for supine positioning, stone size, and stone complexity by Guy score (OR 2.31, 95% CI 1.01–5.29, p = 0.048). Patients in the ultrasound-only group were spared a mean radiation exposure dose of 10 mGy per procedure. Conclusions: Ultrasound-only PCNL is safe and achieves similar stone-free rates compared with fluoroscopy-directed PCNL with the added benefit of avoidance of radiation.
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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 | 0.003 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".