Association between grade of congenital hydronephrosis and time to natural resolution
Bibliographic record
Abstract
Abstract Introduction Standard of care for pregnancy includes a second trimester ultrasound which may lead to incidental findings of fetal uropathies. The most common fetal uropathy is congenital hydronephrosis (CH). There is limited information regarding the natural history of CH. The objective of this study was to describe factors associated with time to natural resolution of CH. Methodology This is a retrospective cohort study of infants with CH using data from a single hospital’s electronic medical record. Between January 2017 and December 2022, infants with CH were identified using ICD-10 codes and were included in the cohort if they had a second ultrasound prior to 2022. Severity of CH was classified using Society for Fetal Urology (SFU) grades. Cox proportional hazards analysis was used to model time to resolution adjusting for age, sex, and initial SFU grade. Results Of 209 infants with CH, 126 were included in the cohort, with males predominating (78%). A total of 168 kidney units were included with eighty-four infants (67%) having only 1 kidney unit affected. In multivariable Cox proportional hazards regression modelling, after adjusting for sex and age at initial ultrasound, initial SFU grade was associated with time to natural resolution (compared to grade 1, adjusted hazard ratios for grades 2, 3 and 4 were: 0.59 [95% CI 0.34-1.01], 0.23 [95% CI 0.12,0.47] and 0.04 [95% CI 0.01,0.14]; p<0.001). Conclusion On average, kidney units with not only SFU 4, but also SFU 3, resolved more slowly than those with low-grade hydronephrosis. These findings will assist in counseling parents of children with CH.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".