What are the Effective Factors in Spontaneous Resolution Rate of Primary Vesicoureteral Reflux: A Meta-Analysis and Systematic Review.
Bibliographic record
Abstract
PURPOSE: This meta-analysis aimed to predict the rate of spontaneous resolution and identify influencing factors among pediatric patients with primary vesicoureteral reflux (VUR). The primary objective was to construct a nomogram to facilitate clinical decision-making in the treatment of primary VUR by assessing the rate of spontaneous resolution and its determinants. MATERIALS AND METHODS: A systematic search was conducted up to September 2023, encompassing databases such as PubMed, Web of Science, Scopus, and the reference lists of relevant studies. Inclusion criteria comprised 33 studies with a total of 8540 pediatric patients. Data extraction was performed independently by two reviewers, with discrepancies resolved by a third reviewer. Risk of bias was assessed using the Newcastle-Ottawa Quality Assessment Form. The analysis included the assessment of various outcomes, such as the rate of spontaneous resolution, and identification of influential factors, including gender, age, laterality, and VUR grade. RESULTS: The pooled spontaneous resolution rate among pediatric patients with primary VUR was 0.42 (95% CI: 0.38 to 0.47, Tau2 = 0.26), demonstrating high heterogeneity (Q = 429.9, df = 32, P < 0.001, I2 = 93%). Egger's regression test indicated no publication bias (p = 0.67). VUR grade emerged as the most significant determinant of spontaneous resolution, with varying rates for different grades: grade 1 (0.80, 95% CI: 0.72-0.86), grade 2 (0.67, 95% CI: 0.60-0.74), grade 3 (0.49, 95% CI: 0.42-0.56), and grade 4 (0.23, 95% CI: 0.18-0.30; Tau2 = 0.28, I2 = 0.49). While differences in gender and laterality were observed, statistical significance was not evident. CONCLUSION: This study provides valuable insights into the spontaneous resolution rate of primary vesicoureteral reflux in pediatric patients. The constructed nomogram, based on VUR grading, serves as a useful tool for clinicians in decision-making. Despite observed variations in gender and laterality, only VUR grading demonstrated statistical significance in influencing spontaneous resolution. Further research is recommended to explore additional factors within larger populations to enhance our understanding of primary VUR resolution dynamics.
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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 | 0.029 | 0.064 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.068 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".