Conditional Survival of Patients With Nonmetastatic Renal Cell Carcinoma: How Cancer-Specific Mortality Changes After Nephrectomy
Bibliographic record
Abstract
BACKGROUND: Conditional survival (CS) may reveal important differences in cancer-specific mortality (CSM) among patients with nonmetastatic renal cell carcinoma (nmRCC). This study assessed CS according to T and N stages in patients treated surgically for nmRCC. PATIENTS AND METHODS: Within the SEER database (2001-2015), all patients with nmRCC treated with either partial or radical nephrectomy were identified. CSM-free estimates according to T and N stage and substage groupings (pT1aN0-pT4N0 and pTanyN1) and multivariable Cox regression models with adjustment for Fuhrman grade and histologic subtype were assessed. RESULTS: According to T and N stage and substage groupings, the following patients were included in the study: 35,966 (46.2%) with pT1aN0 disease; 18,858 (24.2%) with pT1bN0; 5,977 (7.7%) with pT2aN0; 2,511 (3.2%) with pT2bN0; 11,839 (15.2%) with pT3aN0; 1,037 (1.3%) with pT3b-cN0; 402 (0.5%) with pT4N0; and 1,302 (1.7%) with pTanyN1. Conditional CSM-free survival estimates were 98.2% at 1 year versus 98.0% at 10 years of event-free follow-up for patients with pT1aN0 disease, relative to baseline. Conversely, pT4N0/pTanyN1 conditional CSM-free survival estimates were 55.8% at 1 year versus 77.9% at 8 years of event-free follow-up. Attrition due to mortality was highest in patients with pT4N0/pTanyN1 disease. In multivariable Cox regression analyses, T stage, tumor grade, and histologic subtype represented independent predictors, but no interactions were identified. CONCLUSIONS: Tumor stage and its substages represent extremely important determinants of prognosis after lengthy event-free follow-up. The recorded observations have critical importance for physicians regarding patient follow-up and counseling.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".