Minimally invasive surgical treatment for large impacted upper ureteral stones: Ureteroscopic lithotripsy or percutaneous nephrolithotomy?
Bibliographic record
Abstract
INTRODUCTION: The management of patients with large impacted upper ureteral stones is difficult; there is no standard treatment. We compared the outcomes of percutaneous nephrolithotomy (PCNL) and ureteroscopic lithotripsy (UL) to treat large (≥1.5 cm), impacted, upper ureteral stones. METHODS: In total, 86 patients with large impacted upper ureteral stones were included in this study. Of these patients 41 underwent UL and 45 underwent PCNL. The inclusion criteria were: longest diameter of stone ≥1.5 cm, the localization of stone between the lower border of L4 spine and ureteropelvic junction and impacted stone. RESULTS: In the UL group, we were unable to reach the stone in 3 patients because of ureteral stricture and edema despite balloon dilation. Of these 3 patients, we were unable to optimally visualize the stone in 2 patients due to bleeding and mucosal injury following balloon dilation. The stricture was too firm and could not be passed in the third patient. Also in the UL group, 15 patients had stones or big fragments which migrated into the renal collecting system. In the PCNL group, 21 patients had concurrent renal stones <1 cm and stones were successfully removed in all patients. No statistically significant difference was found between groups in terms of operation time. Mean hospital stay was significantly shorter in the UL group. Success rates were 82.3% in the UL group and 97.6% in the PCNL group (p = 0.001). CONCLUSION: The recent study confirms that PCNL is a safe and effective minimally invasive procedure with acceptable complication rates in the treatment of patients with large, impacted upper ureteral 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".