Unclassified renal cell carcinoma: an analysis of 85 cases
Bibliographic record
Abstract
OBJECTIVES: To compare cancer-specific mortality in patients with unclassified renal cell carcinoma (URCC) vs clear cell RCC (CRCC) after nephrectomy, as URCC is a rare but very aggressive histological subtype. PATIENTS AND METHODS: Eighty-five patients with URCC and 4322 with CRCC were identified within 6530 patients treated with either radical or partial nephrectomy at 18 institutions. Of 85 patients with URCC, 55 were matched with 166 of 4322 for grade, tumour size, and Tumour, Node and Metastasis stages. Kaplan-Meier and life-table analyses were used to address RCC-specific survival. Subsequently, multivariate Cox regression analyses were used to test for differences in RCC-specific survival in unmatched samples. RESULTS: Of patients with URCC, 80% had Fuhrman grades III or IV, vs 37.8% for CRCC. Moreover, 36.5% of patients with URCC had pathologically confirmed nodal metastases, vs 8.6% with CRCC. Finally, 54.1% of patients with URCC had distant metastases at the time of nephrectomy, vs 16.8% with CRCC. Despite these differences in the overall analyses, after matching for tumour characteristics, the URCC-specific mortality rate was 1.6 times higher (P = 0.04) in matched analyses and 1.7 times higher (P = 0.001) in multivariate analyses. CONCLUSIONS: These findings indicate that URCC presents with a higher stage and grade, and even after controlling for the stage and grade differences, predisposes patients to 1.6-1.7 times the mortality of CRCC.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".