Multiple Unilateral Subcapsular Cortical Hemorrhagic (MUCH) Cystic Disease of the Kidney: First Case in Brazil of a Possible New Clinical Entity of Unknown Etiology
Bibliographic record
Abstract
Introduction: The occurrence of MUCH Cystic Disease of the Kidney was described in 14 patients from the USA, France, Canada and Japan by Yoshida et al. in 2019. We describe the first similar case in Brazil. Case Description: A 36-year-old hypertensive Brazilian man was referred to our Nephrology Clinic due to nephrotic syndrome (NS) along with acute kidney injury requiring dialysis. An extensive investigation of systemic diseases associated with NS (viral serologies, syphilis, autoimmune diseases and neoplasms) was negative. Abdominal MRI showed multiple subcapsular cortical hemorrhagic cysts in the left kidney with normal right kidney. The cysts measured <1cm on average, the biggest one 2.5cm. The signal was hyperintense on T1 and hypointense on T2-weighted MRI, suggesting hemorrhagic content. Biopsy of the right kidney revealed focal segmental glomerulosclerosis (FSGS), tip lesion variation, added to acute tubular necrosis in recovery phase. Complete remission of NS and recovery of renal function were achieved after 8 weeks of immunosuppressive treatment. Discussion: Yoshida et al described 14 cases with distinctive renal image findings, similar to the presented. No patients had positive family history of kidney disease. 6 of them underwent genetic evaluation and results were negative for renal cystic disease. The characteristics of the cysts (unilateral, subcapsular with hemorrhagic content) suggest a different etiology from that of the classic APKD1/2, ARKD or medullary cystic kidney disease. Whole genome sequencing is currently being processed. None of the patients previously described had NS nor FSGS, suggesting a non-causal association. These clinical and radiological findings might be features of a new non-inherited renal cystic disease.
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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 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.001 | 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".