Conditional Survival of Patients With Nonmetastatic Renal Cell Carcinoma: How Cancer-Specific Mortality Changes After Nephrectomy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it