The value of intraoperative point-of-care urinalysis to predict positive urine cultures and symptomatic postoperative infections during cystoscopic procedures for pediatric patients
Bibliographic record
Abstract
Introduction: We aimed to evaluate intraoperative point-of-care urinalysis (UA) for predicting positive urine cultures and postoperative urinary tract infections (UTIs) in children undergoing cystoscopy, and to assess its potential to reduce unnecessary cultures and antibiotics. Methods: In this retrospective cohort at a tertiary pediatric urology center (August 2023 to April 2024), 62 cystoscopy cases with paired dipstick UA and quantitative culture were analyzed after excluding recent antibiotic use or incomplete data. Dipstick markers —leukocyte esterase and nitrite — were evaluated alone and combined (“either-positive” vs. “both-positive”). Positive culture was defined as ≥10⁵ CFU/mL; postoperative UTI required fever, clinical signs, and a positive culture within seven days. Diagnostic accuracy was assessed by ROC curves and χ² tests. A multivariable logistic regression adjusted for age, sex, procedure, laterality, and clinical condition. A retrospective quality improvement (QI) model estimated reductions in culture orders and empiric antibiotics. Results: Thirty-nine patients (62.9%) were dipstick-negative by the “either-positive” rule; one had a positive culture (negative predictive value [NPV] 97.4%; 95% confidence interval [CI] 86.5–99.9). Of 23 dipstick-positive patients, 13 (56.5%) had positive cultures. In multivariable analysis, “either-positive” dipstick was the sole predictor of culture positivity (odds ratio [OR] 330.2, 95% CI 30.5–3 574.1, p=0.003). QI modeling indicated that restricting cultures to the 23 dipstick-positive specimens would have averted 39 of 62 cultures (62.9%), at the expense of missing one infection (2.6% of uncultured cases). Conclusions: Intraoperative dipstick UA reliably identifies pediatric cystoscopy patients at low risk for postoperative UTI, offering a rapid, cost-effective tool to enhance antimicrobial stewardship and reduce laboratory utilization. This single-center, retrospective study with a modest sample and low event rate may limit generalizability; prospective, multicenter validation is warranted.
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 | 0.007 |
| 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 | 0.001 | 0.001 |
| 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".