Efficacy of dextranomer hyaluronic acid and polyacrylamide hydrogel in endoscopic treatment of vesicoureteral reflux: A comparative study
Bibliographic record
Abstract
INTRODUCTION: Various bulking agents are available for vesicoureteral reflux (VUR) endoscopic treatment, but their inconsistent success rates and costs are concerns for urologists. Recently, polyacrylamide hydrogel (PAHG) has been shown to have a good overall success rate, which seems comparable to dextranomer hyaluronic acid (Dx/HA), currently the most popular bulking agent. Our objective was to compare the short-term success rate of PAHG and Dx/HA for VUR endoscopic treatment in children. METHODS: We performed a prospective non-randomized study using PAHG and Dx/HA to treat VUR grades I to IV in pediatric patients. All patients underwent endoscopic sub-ureteric injection of PAHG or Dx/HA, using the double-HIT technique, followed by a 3-month postoperative renal ultrasound and voiding cystourethrogram. Treatment success was defined as the absence of de novo or worsening hydronephrosis and absence of VUR. RESULTS: A total of 90 pediatric patients underwent an endoscopic injection: 45 patients (78 ureters) with PAHG and 45 patients (71 ureters) with Dx/HA. The mean injected volume of PAHG and Dx/HA was 1.1 mL and 1.0 mL, respectively. The overall success rate 3 months after a single treatment was 73.1% for PAHG and 77.5% for Dx/HA. Postoperatively, 1 patient in each group presented with acute pyelonephritis and 2 patients in the Dx/HA group developed symptomatic ureteral obstruction. CONCLUSION: Success rates of PAGH and Dx/HA in endoscopic injections for VUR treatment were comparable. The rate of resolution obtained with Dx/HA was equivalent to those previously published. The lower cost of PAHG makes it an interesting option.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".