Node‐positive renal cell carcinoma in the absence of distant metastases: predictors of cancer‐specific mortality in a population‐based cohort
Bibliographic record
Abstract
UNLABELLED: Nodal metastases, even in the absence of distant metastases, portend a bad prognosis. The percentage of positive nodes (PPN) represents an important predictor of cancer-specific mortality (CSM) in patients in the group T(any) N(1) M(0) . In consequence, universal inclusion of PPN should be considered in prospective and retrospective CSM analyses. OBJECTIVES: To examine the outcomes of patients with node-positive renal cell carcinoma (RCC) in the absence of distant metastases in a large population-based cohort of patients To examine the ability of standard risk factors to predict cancer-specific mortality (CSM). PATIENTS AND METHODS: Using the Surveillance, Epidemiology, and End Results database, a total of 799 patients with RCC nodal metastases and absence of distant metastases undergoing nephrectomy were identified. Univariable and multivariable analyses was performed with the aim of identifying independent predictors of CSM in this cohort of patients. Specifically, we examined the effect of the number of removed nodes (NRN), the number of positive nodes (NPN) and the percentage of positive nodes (PPN) on CSM. RESULTS: Actuarial survival estimates showed that 53.2, 37.8 and 25.7% of patients survived at 24, 60 and 120 months after nephrectomy. In Kaplan-Meier analyses, NRN failed to clearly discriminate between recorded CSM rates (log rank P = 0.07). Discrimination was noted when CSM was stratified according to NPN (log rank P = 0.02) and PPN (log rank P = 0.001). In multivariable analyses, age, Fuhrman grade, histological subtype, T stage and PPN were independent predictors of CSM. CONCLUSIONS: Our data indicate that CSM of patients with exclusive nodal metastases differs according to PPN. Consequently, PPN warrants consideration in future prognostic schemes.
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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 | 0.001 |
| 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 | 0.000 |
| 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".