The effects of previous open renal stone surgery types on PNL outcomes
Bibliographic record
Abstract
INTRODUCTION: Our aim was to demonstrate the effect of insicion of renal parenchyma during open renal stone surgery (ORSS) on percutaneous nephrolithotomy (PNL) outcomes. METHODS: Patients with history of ORSS who underwent PNL operation between June 2005 and June 2015 were analyzed retrospectively. Patients were divided into two groups according to their type of previous ORSS. Patients who had a history of ORSS with parenchymal insicion, such as radial nephrotomies, anatrophic nephrolithotomy, lower pole resection, and partial nephrectomy, were included in Group 1. Other patients with a history of open pyelolithotomy were enrolled in Group 2. Preoperative characteristics, perioperative data, stone-free status, and complications were compared between the groups. Stone-free status was defined as complete clearance of stone(s) or presence of residual fragments smaller than 4 mm. The retrospective nature of our study, different experience level of surgeons, and lack of the evaluation of anesthetic agents and cost of procedures were limitations of our study. RESULTS: 123 and 111 patients were enrolled in Groups 1 and 2, respectively. Preoperative characteristics were similar between groups. In Group 1, the mean operative time was statistically longer than in Group 2 (p=0.013). Stone-free status was significantly higher in Group 2 than in Group 1 (p=0.027). Complication rates were similar between groups. Hemorrhage requiring blood transfusion was the most common complication in both groups (10.5% vs. 9.9%). CONCLUSIONS: Our study demonstrated that a history of previous ORSS with parenchymal insicion significantly reduces the success rates of PNL procedure.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".