The effect of age on cancer-specific mortality in patients with small renal masses: A population-based analysis
Bibliographic record
Abstract
INTRODUCTION: Contemporary data regarding the effect of age, especially in elderly patients, on cancer-specific mortality (CSM) for pT1a renal cell carcinoma (RCC) are lacking. The objective of the current study is to evaluate CSM in a large population-based cohort of surgically treated pT1a RCC patients according to age groups. METHODS: Within the Surveillance Epidemiology and End Results database (2000-2013), we identified 37 121 pT1a RCC patients who underwent either partial or radical nephrectomy. The population was stratified into five groups according to decades: <50, 50-59, 60-69, 70-79, and ≥80 years. The effect of age on CSM was evaluated using competing risks regression models according to Fuhrman grade (FG). Analyses were repeated in clear-cell RCC (ccRCC). RESULTS: Patients aged 50-59 (9615), 60-69 (10 762), 70-79 (7096), and ≥80 (1789) years demonstrated higher rate of CSM compared to patients aged <50 (7856) years (hazard ratios [HR] 2.11, 3.04, 4.47, and 7.56, respectively; all p<0.001). The effect of age on CSM in FG 1-2 patients resulted in HRs ranging from 2.01-8.23 for the same age decades (all p< 0.001). Similarly, the effect of age on CSM in FG 3-4 patients resulted in HRs ranging from 2.38-5.92, respectively (all p<0.001). Virtually the same results were recorded in ccRCC patients. CONCLUSIONS: Older age is associated with higher CSM in surgically treated patients with pT1a RCC. This effect seems to be more pronounced in patient with FG 1-2 disease. This observation should be considered when making treatment decisions in elderly patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".