A Rare Cause of Polyuria and Polydipsia in a Patient With Cystic Renal Disease: Maturity-Onset Diabetes of the Young Type 5
Bibliographic record
Abstract
Hepatocyte nuclear factor-1beta (HNF-1beta) is a transcription factor that is responsible for the development of kidney, pancreas, liver and genitourinary tract. Affected individuals may present a variety of renal developmental abnormalities and/or maturity-onset diabetes of the young type 5 (MODY 5). Here we report a boy with autosomal recessive polycystic kidney disease (ARPKD) diagnosed in neonatal period who developed insulin-dependent diabetes at the age of 11. He presented with poliuria and polydipsia. The diagnosis of ARPKD was made in neonatal period based on the findings of large hyperechogenic kidneys in antenatal ultrasound and no history of renal disease in parents. Laboratory investigations revealed hyperglycemia, glycosuria, and a reduced glomerular filtration rate (GFR). Based on autoantibody-negative diabetes and low-dose insulin requirement in addition to renal anomalies, he was suspected to have MODY 5. Genetic studies identified a known heterozygous HNF1B gene mutation (S148L) compatible with an MODY 5 phenotype. As a result, MODY 5 should be considered in children with developmental kidney disease and hyperglycemia. Also HNF-1beta mutations should be suspected in patients with undefined cystic kidney disease especially when associated with other systemic findings as in our case. World J Nephrol Urol. 2016;5(3):67-70 doi: http://dx.doi.org/10.14740/wjnu272w
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".