Quantitative Voiding Cystourethrogram Features Predict Detecting Photopenic Renal Parenchyma Defects on Radionuclide Imaging in Patients With Vesicoureteral Reflux
Bibliographic record
Abstract
OBJECTIVE: To evaluate the utility of quantitative features on voiding cystourethrogram (VCUG) to predict the presence of photopenic renal defects (PRDs) consistent with scarring. PRDs remain an indicator of kidney damage in patients with vesicoureteral reflux (VUR), and their presence influences management and long-term outcomes. METHODS: We identified children with primary VUR who underwent a VCUG and a nuclear renal scan within 6 months at our institution between 2016 and 2021. Quantitative VUR features (qVUR) were extracted, including ureteral width (proximal, distal, and maximum) and ureteral tortuosity. Logistic regression models were developed to predict PRDs employing clinical features, VCUG indication, and VUR laterality (baseline model); the addition of VUR grade (grade model); the addition of qVUR (qVUR model); and the addition of both grade and qVUR (combined model). RESULTS: In total, 130 children (163 renal units) were included, with median age at VCUG of 20 months. PRD+ and PRD- groups differed significantly in age (47 vs 7 months, P <.001), high-grade reflux (54% vs 29%, P = .001), and recurrent urinary tract infection history (57% vs 26%, P <.001). Model performance was highest for the combined model (AUROC 0.84, AUPRC 0.87), followed by qVUR (AUROC 0.80, AUPRC 0.85) and grade (AUROC 0.81, AUPRC 0.82). This analysis was limited by the absence of clinical endpoints incorporated into model development, including the development of chronic kidney disease and hypertension. CONCLUSION: Quantitative VCUG features show promise in refining predictive models to identify children with VUR who are at risk for PRDs suggestive of renal scarring, outperforming traditional metrics.
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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 | 0.001 | 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".