Normal black kidney
Bibliographic record
Abstract
A black kidney has 3 major differential diagnoses: hemosiderosis, lipofuscin pigment and melanotic renal cell carcinoma. Excluding lipofuscin, the other 2 are accompanied by an abnormal renal function. We report on a 25-year-old man who intended to donate a kidney to his cousin. On the operating room table when we incised the left flank region and exposed the kidney, we found a firm and black kidney so the operation was cancelled due to potential vascular injuries. Days after the incomplete procedure, we reviewed the donor's biochemistry and imaging to reassess his renal function, but the results showed quite normal renal function again. The result of Ham test was also negative. Two weeks later, we began the operation, removed the same left kidney and found that it was in the same conditions as it was before. We took the opportunity to send needle biopsies of the kidney for histopathologic analysis. The analysis showed a melanotic kidney without pathological changes in glomeruli and interstitium and vessels. A black kidney may result in hemosiderin, lipofuscin or melanin deposits in the kidney, which can confirm the diagnosis; however, special tests for underlying disease and renal function should be considered. Some causes of black kidney lead to abnormal function, but our patients's kidney returned to normal.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".