Complications, Re-Intervention Rates, and Natural History of Residual Stone Fragments After Percutaneous Nephrolithotomy
Bibliographic record
Abstract
INTRODUCTION: The management of residual fragments (RFs) that persist after percutaneous nephrolithotomy (PCNL) has been poorly studied. Fragments have the potential to grow or cause symptoms. The aim of this study was to follow patients with fragments after PCNL to identify predictors of stone-related events (re-interventions and complications) after PCNL. PATIENTS AND METHODS: Data were retrospectively collected from patients who underwent surgery from 2008 to 2013 at our hospital. Patients with fragments of any size on postoperative day 1 computed tomography of the kidney, ureter, and bladder radiograph (CT-KUB) were included, and patients with planned secondary interventions were excluded. Subgroup analysis was performed on subjects with CT-plain X-ray to determine fragment growth or passage. RESULTS: Of the 658 patients who received a postoperative CT-KUB on day 1, 299 patients (45%) had fragments that were 1 mm or larger. From this, 263 patients met the study criteria and were included. The size of fragments, using a 4 mm cutoff, did not predict the passage of fragments (p = 0.173) or growth (p = 0.572). On multivariable logistic regression analysis, previous history of renal stones and size of fragment were found to be predictive for stone-related events (p = 0.002 and 0.027, respectively). Kaplan-Meier analysis identified patients with fragments >4 mm having a shorter survival time before the occurrence of stone-related events (p = 0.044). CONCLUSIONS: The true stone-free rate was 55% after PCNL. However, 82.5% were stone free or had RFs 4 mm or less, which correlates with previous studies. Larger RFs had higher rates of stone-related events and shorter time to occurrence of stone-related events. The growth and spontaneous passage of RFs was independent of RF size, emphasizing the importance of obtaining a stone-free status after PCNL.
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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 | 0.005 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".