Effect of shockwave lithotripsy before percutaneous nephrolithotomy on the risk of renal arteriovenous fistula
Bibliographic record
Abstract
INTRODUCTION: This study aimed to assess the impact of shockwave lithotripsy (SWL) on the incidence of renal arteriovenous fistula (AVF) following percutaneous nephrolithotomy (PCNL). METHODS: We retrospectively analyzed data from patients who underwent PCNL and/or SWL for kidney stones, as well as angioembolization for renal AVF, between January 2000 and December 2024. Patients with inadequate followup or insufficient medical records were excluded. Patients were divided into two groups: the first group included those who did not undergo SWL before PCNL, while the second group included those who underwent SWL. Various patient factors, stone characteristics, and surgical features were compared between the groups. RESULTS: We reviewed data from 851 PCNL patients, 3043 SWL patients, and 31 patients who underwent angioembolization for renal AVF. Fourteen patients were excluded due to insufficient data. A total of seventeen patients (1.2%) developed renal AVF after PCNL, with 13 (76.5%) of them having undergone prior SWL at the same renal unit. In the group with a history of SWL before PCNL, the odds ratio calculated for the occurrence of AVF was 190.4 (95% confidence interval 55.1-657.1, p<0.001). CONCLUSIONS: AVF is a serious complication that occurs after vascular damage. Both SWL and PCNL are among the treatments that can cause this complication. Due to their additive effects, performing SWL and PCNL consecutively in a short period appears to increase the risk of developing AVF. Careful patient management is essential to minimize this risk.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".