Bibliographic record
Abstract
Patients with end-stage renal disease and acquired cystic kidney disease are at increased risk of development of renal tumors, and the histologic spectrum of tumors in this setting differs from that of the general population. Two histologies enriched in this context include acquired cystic kidney disease–associated renal cell carcinoma and clear cell papillary renal cell carcinoma, both now considered distinct tumor entities in the International Society of Urological Pathology Vancouver Classification of Renal Neoplasia and 2016 World Health Organization Classification. Acquired cystic kidney disease–associated renal cell carcinoma prototypically demonstrates cribriform architecture, eosinophilic cells, prominent nucleoli, and intratumoral calcium oxalate crystals. However, some characteristics overlap with papillary renal cell carcinoma, including diffuse labeling for α-methylacyl-CoA racemase. Cysts in acquired cystic kidney disease (“atypical” cysts) are thought to represent its precursor lesion, often sharing similar cytology and architecture. Clear cell papillary renal cell carcinoma, in contrast, may closely mimic clear cell renal cell carcinoma; however, its behavior is highly favorable, with no convincing examples of metastases to date. This diagnosis can be supported by immunohistochemical positivity for cytokeratin 7, carbonic anhydrase IX, GATA3, and high-molecular-weight keratin, with lack of reactivity for AMACR and CD10. Despite enrichment for these tumor types, a wide variety of renal tumors also occur in end-stage renal disease, including more well-known histologies, such as clear cell and papillary renal cell carcinomas, as well as papillary adenoma and a newly recognized subtype of vascular tumor, anastomosing hemangioma.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".