Infectious Complications, Healthcare Resource Use, and Medical Costs Associated with Delays in Percutaneous Nephrolithotomy Among Patients with Stone Disease and Ureteral Stent Placement
Bibliographic record
Abstract
Purpose: The relationship between ureteral stent duration before percutaneous nephrolithotomy (PCNL) and infectious complications, admissions, imaging, and medical costs was evaluated. Materials and Methods: Patients who underwent PCNL within 6 months of ureteral stent placement were identified from commercial claims, categorized by time to treatment (0–30, 31–60, and >60 days), and followed 1-month post-PCNL. The effect of delayed treatment on inpatient admissions, infectious complications (pyelonephritis/sepsis), and imaging utilization was evaluated with logistic regression. A generalized linear model evaluated the effect of delayed treatment on medical costs. Results: Among 564 patients with PCNL and meeting the inclusion criteria (mean age 50; 55% female; 45% from South), mean (standard deviation) time to surgery was 48.8 (41.8) days. Less than half (44.3%; n = 250) underwent PCNL within 30 days of ureteral stent placement, 27.0% ( n = 152) between 31 and 60 days, and 28.7% ( n = 162) >60 days. Time to PCNL was significantly associated with inpatient admissions (>60 vs ≤30 days odds ratio [OR] 1.97, 95% confidence interval [CI] 1.29–3.01, p = 0.0016), infectious complications (>60 vs ≤30 days OR 2.43, 95% CI 1.55–3.81, p = 0.0001), imaging utilization (31–60 vs ≤30 days OR 1.56, 95% CI 1.02–2.38, p = 0.0383; >60 vs ≤30 days OR 2.01, 95% CI 1.31–3.06, p = 0.0012), and medical costs (31–60 vs ≤30 days OR 1.27, 95% CI 1.08–1.49, p = 0.0048; >60 vs ≤30 days OR 1.46, 95% CI 1.24–1.71, p < 0.0001). Conclusions: Compared with PCNL within 30 days, patients undergoing PCNL >30 days after ureteral stent placement had increased likelihood of infectious complications, resource use, and medical costs. These results may inform health care resource utilization and PCNL prioritization.
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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 | 0.009 |
| 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 | 0.001 |
| 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".