Surgical Management of Kidney and Ureteral Stones: AUA Guideline (2026). Part II: Evaluation and Treatment of Patients With Kidney and/or Ureteral Stones
Bibliographic record
Abstract
PURPOSE: This Guideline covers the surgical management of patients with kidney and/or ureteral stones and is intended for clinicians evaluating and managing patients with this disease. The summary presented herein represents Part II of the 3-part series dedicated to Surgical Management of Kidney and Ureteral Stones. Please refer to Parts I and III for additional information on this topic. MATERIALS AND METHODS: This systematic review was conducted in 2 planned stages, including a search for systematic reviews followed by a search for primary literature. OVID was used to systematically search MEDLINE and EMBASE databases for articles evaluating surgical management of kidney and ureteral stones. The Panel selected control articles that were deemed relevant and the articles were compared with the literature search strategy output. The methodologist then updated the strategy as necessary to capture all control articles. Databases were searched for studies published from January 2000 through May 2025 (week 20). In addition to the MEDLINE and EMBASE databases searches, reference lists of included systematic reviews and primary literature were scanned for potentially useful studies. RESULTS: The Panel addressed adult and pediatric patients with kidney and/or ureteral stones for whom surgical intervention may be indicated. Each statement herein addressed a particular patient scenario for which the choice of surgical intervention was reviewed and justified. In addition, the Panel reviewed and analyzed the utility of specific surgical techniques, technologies, or medications aimed at improving patient outcomes. CONCLUSIONS: Selection of optimal treatment modalities for patients with kidney and/or ureteral stones is determined by patient factors, urinary tract anatomy, and stone characteristics and are guided by shared decision-making that additionally takes into account patient goals and preferences, resource availability, and physician expertise. This Guideline serves as a resource for clinicians and patients to provide the best available evidence on which to base discussions with patients in a shared decision-making process to arrive at appropriate treatment decisions.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".