Stones in urinary diversions: update on medical and surgical issues
Bibliographic record
Abstract
PURPOSE OF REVIEW: Patients who undergo urinary tract diversion are at an increased risk of urolithiasis for various reasons. The purpose of this article is to provide an up-to-date summary of the progress that has been made in the pathogenesis, diagnosis and treatment of stones in patients with urinary diversions. Finally, we will provide recommendations for follow-up in patients with urinary diversions who develop urinary tract calculi. RECENT FINDINGS: In contemporary studies, the incidence of urolithiasis in patients with urinary diversion appears to be decreasing. Computed tomography scanning has been shown to be superior to ultrasound in the diagnosis of calculi in such patients. Endourological procedures have become the mainstay of therapy for stones in patients with urinary diversions. Since the introduction of extracorporeal shock wave lithotripsy, percutaneous nephrolithotripsy and ureteroscopy, the need for open surgery has decreased, even in this anatomically unique and surgically challenging patient population. SUMMARY: Urolithiasis is an established long-term complication of urinary diversion. In recent years, significant advances have been made in the pathogenesis, diagnosis and treatment of such stones. As a result of potential stone-related complications, we recommend lifelong surveillance for all patients with urinary stones and diversions, with medical therapy when indicated, in order to minimize these complications.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".