Large Kidney Cysts in <i>HNF1B</i> Nephropathy Mimicking Autosomal Dominant Polycystic Kidney Disease
Bibliographic record
Abstract
Rationale: Hepatocyte nuclear factor 1 beta ( HNF1B) nephropathy is a rare autosomal dominant monogenic kidney disease. We present a case mimicking autosomal dominant polycystic kidney disease (ADPKD), highlighting the phenotypic heterogeneity of HNF1B-related disease. Presenting concerns of the patient: A 37-year-old man presented with hypertensive urgency, accompanied by flank pain and abdominal distension. Despite the absence of familial kidney disease, imaging revealed large bilateral kidney cysts resembling ADPKD. Diagnosis: We initially suspected de novo ADPKD. However, negative genetic testing results for PKD1 and PKD2 led to a 43-gene cystic kidney sequencing panel which identified a deletion encompassing the entire HNF1B gene. Intervention: To alleviate discomfort caused by the kidney cysts, ultrasound-guided aspiration and foam sclerotherapy were performed. Tolvaptan, used for treating high-risk ADPKD, was not prescribed after confirming the diagnosis was HNF1B nephropathy. Outcomes: A diagnosis of HNF1B nephropathy was reached following gene panel testing. Abdominal symptoms improved following cyst aspiration and foam sclerotherapy. Novel findings: HNF1B nephropathy has a variable presentation but can lead to cysts appearing like ADPKD. A 43-gene cystic kidney sequencing panel identified the diagnosis in this uncertain case.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 |
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".