Acquired cystic kidney disease: rapid progression from small to enlarged kidneys simulating adult polycystic kidney disease.
Bibliographic record
Abstract
A 57-year-old man on chronic hemodialysis presented marked bilateral renal enlargement due to acquired cystic kidney disease (ACKD). He had been on hemodialysis for less than 3 years only (14 months prior to receiving a functional renal transplant which lasted 8 years, followed by 18 additional months of dialysis), before the diagnosis of ACKD was made following an episode of flank pain with gross hematuria. The marked changes in kidney appearance during this 11-year period were documented by serial ultrasound examination showing the kidneys to be of near-normal size before the start of dialysis (> or =10 cm in 1986), then shrunken and contracted 5 years later while having a functioning renal transplant (<5 cm in 1991), and markedly enlarged reaching the size of adult polycystic kidney disease after returning to dialysis (>13 cm in 1997). Since the risk of ACKD increases with duration of dialysis, we sought additional predisposing factors in this unusual case and found that 2 years after renal transplantation, the patient was diagnosed with breast cancer for which he was treated with surgical excision and tamoxifen. Based on ultrasound evidence that the tamoxifen treatment preceeded the appearance of the renal cystic changes, we wonder whether this drug may have played a role in the rapid development of ACKD.
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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) | 0.001 | 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 | 0.000 |
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".