Etiologies and outcomes of prenatally diagnosed hyperechogenic kidneys
Bibliographic record
Abstract
OBJECTIVES: To determine etiologies and outcomes of fetal hyperechogenic kidneys (HEK). METHODS: We conducted a retrospective chart review of HEK in British Columbia (January 2013-December 2019) and literature review. RESULTS: We identified 20 cases of HEK without other anomalies (isolated) in our provincial cohort, one was lost to follow-up. Eight had testable genetic etiologies (autosomal dominant polycystic kidney disease [ADPKD], autosomal recessive polycystic kidney disease [ARPKD], Bardet-Biedl syndrome [BBS], and HNF1B-related disorder). The remaining seven did not have an identifiable genetic etiology. Of cases without a genetic etiology with postnatal follow-up (n = 6) there were no abnormalities of blood pressure, creatinine/estimated glomerular filtration rate or urinalysis identified with follow-up from 2-71 months. We report 11 cases with extrarenal anomalies (nonisolated), with outcomes and etiologies. We identified 224 reported cases of isolated HEK in the literature. A potentially testable genetic etiology was found in 128/224 (57.1%). The neonatal death rate in those with testable etiologies was 17/128 (13.3%) compared to 2/96 (2.1%) when testable etiologies were excluded. CONCLUSIONS: Genetic etiologies (ARPKD, ADPKD, BBS, HNF1B-related disorder, Beckwith-Wiedemann syndrome, tubular dysgenesis, familial nephroblastoma, and cytogenetic abnormalities) account for approximately half of prenatally isolated HEK; once excluded there are few neonatal deaths and short-term renal outcomes may be normal. There remains a paucity of knowledge about long-term renal outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".