Spontaneous Resolution of Primary Obstructive Megaureter: Risk Stratification and Prediction Based on Early Sonographic Factors
Bibliographic record
Abstract
PURPOSE: We describe and develop predictors for spontaneous resolution of primary obstructive megaureter (POM) from early ultrasound (US) measures. MATERIALS AND METHODS: Children referred to our institution between 2007 and 2023 for POM were reviewed. POM was defined as hydroureteronephrosis with ureteral dilation > 7 mm. We excluded patients with other etiologies for upper tract dilation. Resolution was defined as decrease in hydronephrosis to < 10 mm anteroposterior diameter (APD) or Society for Fetal Urology grade ≤ 2 or ≤ 7 mm in hydroureter. Patients were censored if they underwent surgical intervention or were lost to follow-up before documenting resolution. Kaplan-Meier curves were drawn to illustrate the cumulative resolution rate and determine univariate associations. Cox proportional hazards regression was performed to identify significant predictors for early resolution, and C index was calculated. RESULTS: A total of 159 patients were included, with a median index age of 2 months and a median follow-up of 30 months. Of these, 89 patients reached spontaneous resolution during monitoring, and likelihood of surgical indication at 1 year from US was 30%. APD > 15 mm, high-grade hydronephrosis, ureteral dilation > 10 mm, and ureter tortuosity at presentation were associated with a lower likelihood of resolution for individual Kaplan-Meier curves. A Cox regression model trained on these predictors achieved an adjusted C index of 0.68, and low APD remained associated with a higher likelihood of resolution. CONCLUSIONS: Early sonographic features in POM, specifically APD, are associated with the likelihood of spontaneous resolution. Patients with high-risk features at first US warrant closer follow-up.
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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 | 0.008 |
| 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 | 0.000 |
| 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".