Swiss Consensus on Prenatal and Early Postnatal Urinary Tract Dilation: Practical Approach and When to Refer
Bibliographic record
Abstract
Urinary tract dilations (UTDs) are the most frequent prenatal renal anomaly. The spectrum of etiologies causing UTD ranges from mild spontaneously resolving obstruction to severe upper and lower urinary tract obstruction or reflux. The early recognition and management of these anomalies allows for improved renal endowment prenatally and ultimately better outcome for the child. The role of the general obstetrician and pediatrician is to recognize potential prenatal and postnatal cases addressed to their practice and to refer patients to specialized pediatric nephrology and urology centers with a sense of the urgency of such a referral. The aim of this paper is to offer clinical recommendations to clinicians regarding the management of neonates and children born with prenatally detected UTD, based on a consensus between Swiss pediatric nephrology centers. The aim is to give suggestions and recommendations based on the currently available literature regarding classifications and definitions of prenatal and postnatal UTD, etiologies, prenatal and postnatal renal function evaluation, investigations, antibiotic prophylaxis, and the need for referral to a pediatric nephrologist and/or urologist. The overarching goal of a systematic approach to UTD is to ultimately optimize kidney health during childhood and improve long-term renal function prognosis.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".