Renal pelvis urine density as a predictor of infectious complications after semi-rigid ureterorenoscopy for ureteral stone treatment
Bibliographic record
Abstract
INTRODUCTION: This study aimed to investigate whether renal pelvis urine density (RPUD) serves as a reliable predictor of postoperative infectious complications in patients undergoing semi-rigid ureterorenoscopy (URS) for ureteral stone treatment. METHODS: We retrospectively reviewed 1104 patients who underwent semi-rigid URS for ureteral stones. Patients were divided into two groups based on whether they developed postoperative infections within one month (n=64) or not (n=1040). Demographic variables (age, sex, body mass index), comorbidities, stone characteristics (location, size, density), and operative parameters (operation time, stent/catheter placement) were recorded. Renal pelvis urine density was measured in Hounsfield units on preoperative imaging. RESULTS: Of the 1104 patients, 64 (5.8%) developed postoperative infections. The median RPUD was significantly higher in the infectious group (10 [5-17] HU) compared to the non-infectious group (4 [2-6] HU; p=0.001). On multivariate analysis, sex (odds ratio [OR] 4.001, 95% confidence interval [CI] 2.231-7.174, p=0.001), body mass index (OR 0.920, 95% CI 0.860-0.984, p=0.015), operation time (OR 0.963, 95% CI 0.932-0.996, p=0.028), and RPUD (OR 0.809, 95% CI 0.771-0.849, p=0.001) were independent predictors of postoperative infection. The area under the curve was 0.784 (p<0.001, 95% CI 0.711-0.857), demonstrating good discriminative ability. When a cutoff value of 6.35 was applied, the sensitivity and specificity were 71.9% and 76.9%, respectively, for predicting postoperative infections. CONCLUSIONS: Higher RPUD is significantly associated with an increased risk of infectious complications following semi-rigid URS for ureteral stones. Incorporating RPUD into preoperative assessments may help identify high-risk patients and optimize perioperative management to reduce infection-related morbidity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".