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Record W4413435259 · doi:10.1097/mop.0000000000001485

Antenatal hydronephrosis: an updated review on postnatal care and management

2025· article· en· W4413435259 on OpenAlexaff
Julie Wong, Mandy Rickard, Joana Dos Santos, Armando J. Lorenzo

Bibliographic record

VenueCurrent Opinion in Pediatrics · 2025
Typearticle
Languageen
FieldMedicine
TopicPediatric Urology and Nephrology Studies
Canadian institutionsHospital for Sick ChildrenUniversity of British Columbia
Fundersnot available
KeywordsHydronephrosisMedicineEtiologyBroad spectrumIntensive care medicineEpidemiologyNatural historyDiseasePediatricsUrinary systemPathologyInternal medicine

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Antenatal hydronephrosis is the most common prenatally detected fetal anomaly and represents a spectrum of diseases from benign and self-limiting to significant uropathies. This review aims to provide an updated overview of antenatal hydronephrosis, organized by etiology, outlining epidemiology, diagnostic approach, and clinical implications of common and uncommon causes of hydronephrosis. We also explore management strategies, long-term kidney outcomes, and emerging research areas. RECENT FINDINGS: Novel research has focused on machine learning models to interpret imaging and predict the natural history of hydronephrosis and urinary tract dilation. Further studies aim to individualize care, reduce the use of antibiotic prophylaxis, and minimize the use of invasive imaging studies. SUMMARY: Categorizing and risk-stratifying the underlying etiology of antenatal hydronephrosis is important to understand the need for further work-up, select appropriate treatment options, and predict outcomes for patients affected by this broad spectrum of disease.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.375
Threshold uncertainty score0.750

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.025
GPT teacher head0.348
Teacher spread0.323 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreReview

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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