Characterization of the prenatal renal phenotype associated with 17q12, <i>HNF1B</i>, microdeletions
Bibliographic record
Abstract
OBJECTIVE: Recurrent deletions involving 17q12 are associated with a variety of clinical phenotypes, including congenital abnormalities of the kidney and urinary tract (CAKUT), maturity onset diabetes of the young, type 5, and neurodevelopmental disorders. Structural and/or functional renal disease is the most common phenotypic feature, although the prenatal renal phenotypes and the postnatal correlates have not been well characterized. METHOD: We reviewed pre- and postnatal medical records of 26 cases with prenatally or postnatally identified 17q12/HNF1B microdeletions (by chromosomal microarray or targeted gene sequencing), obtained through a multicenter collaboration. We specifically evaluated 17 of these cases (65%) with reported prenatal renal ultrasound findings. RESULTS: Heterogeneous prenatal renal phenotypes were noted, most commonly renal cysts (41%, n = 7/17) and echogenic kidneys (41%), although nonspecific dysplasia, enlarged kidneys, hydronephrosis, pelvic kidney with hydroureter, and lower urinary tract obstruction were also reported. Postnatally, most individuals developed renal cysts (73%, 11/15 live births), and there were no cases of end-stage renal disease during childhood or the follow-up period. CONCLUSION: Our findings demonstrate that copy number variant analysis to assess for 17q12 microdeletion should be considered for a variety of prenatally detected renal anomalies. It is important to distinguish 17q12 microdeletion from other etiologies of CAKUT as the prognosis for renal function and presence of associated findings are distinct and may influence pregnancy and postnatal management.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 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".