Avoiding routine postoperative voiding cystourethrogram: Predicting radiologic success for endoscopically treated vesicoureteral reflux
Bibliographic record
Abstract
INTRODUCTION: Variability in the success rates for the endoscopic correction of vesicoureteral reflux (VUR) has prompted a debate regarding the use of routine postoperative voiding cystourethrogram (VCUG). This study examines the predictive performance of intraoperative mound morphology (IMM) and the presence of a postoperative ultrasound mound (PUM) on radiologic success, as well as investigates the role of using these two predictive factors as a composite tool to predict VUR resolution after endoscopic treatment. METHODS: This retrospective study included children with primary VUR who underwent endoscopic correction with a double hydrodistension-implantation technique (HIT) and dextranomer/hyaluronic acid copolymer. IMM was assessed intraoperatively. The presence of a PUM and VUR resolution were assessed by postoperative ultrasound (US) and VCUG, respectively. Radiologic success was defined as VUR resolution. RESULTS: A total of 70 children (97 ureters) were included in the study. The overall radiologic success rate was 83.5%. There was no statistically significant association between radiologic success and IMM (85.2% with excellent and 87.5% with "other" morphology; p=0.81). The sensitivity and specificity of PUM for radiologic success in this study was 98% and 71%, respectively, while the sensitivity and specificity of the combined prediction model were 81.9% and 85.7%, respectively. CONCLUSIONS: We objectively demonstrated that IMM was a poor predictor of radiologic success and should be used with caution. In addition, the performance of a combined prediction model was inferior to the presence of a PUM alone. As such, selective use of postoperative VCUG may be guided solely by the presence of a PUM.
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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 | 0.000 |
| 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".