Metastatic Renal Cell Carcinoma without Evidence of a Primary Renal Tumour
Bibliographic record
Abstract
Although metastases are common in patients with renal cell carcinoma (rcc), it is extremely rare for patients to present with metastatic rcc (mrcc) without evidence of a primary mass in the kidney. Two cases of mrcc with no detectable primary renal mass are reported here. Both patients had bilateral native kidneys in situ and no significant prior urologic history. The first patient presented with a hip fracture and was found to have multiple radiologic bony and lung metastases. Biopsy of a mass involving the pubic bone demonstrated clear cell mrcc. Multiple scans by computed tomography (ct) and confirmatory imaging by magnetic resonance demonstrated no renal mass. This first patient had disease stabilization for 18 months on sunitinib and was still alive at last follow-up. The second patient was diagnosed with clear-cell mrcc after thickened synovium was discovered and biopsied during a knee arthroplasty. Multiple scans by ct in this second patient demonstrated no primary renal mass. Sunitinib and radiotherapy to the knee lesion were initiated, but unfortunately, the patient deteriorated clinically and passed away from disease progression shortly after diagnosis. Because of the rare nature of these cases, a standardized course of action has not yet been established. However, we hypothesize that it is reasonable to manage metastases in these patients by following established mrcc protocols.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".