Age at diagnosis is a determinant factor of renal cell carcinoma– specific survival in patients treated with nephrectomy
Bibliographic record
Abstract
OBJECTIVE: Based on combined data for 4880 patients, 2 previous studies reported that advanced age is a predictor of increased renal cell carcinoma-specific mortality (RCC-SM). We explored the effect of age in cubic spline analyses to identify the age groups with the most elevated risk for renal cell carcinoma (RCC). METHODS: Our study included 3595 patients from 14 European centres who had partial or radical nephrectomies. We used the Kaplan-Meier method to compile life tables, and we performed Cox regression analyses to assess RCC-SM. Covariates included age at diagnosis, sex, TNM (tumour, node, metastasis) stage, tumour size, Fuhrman grade, symptom classification and histological subtype. RESULTS: Age ranged from 10 to 89 (mean 63, median 67) years. The median duration of follow-up was 2.9 years. The median survival for the cohort was 13.4 years. Stage distribution was as follows: 1915 patients (53.3%) had stage I disease, 388 (10.8%) had stage II, 895 (24.9%) had stage III and 397 (11.0%) had stage IV disease. In multivariate analyses, we coded age at diagnosis as a cubic spline, and it achieved independent predictor status (p < 0.001). The risk of RCC-SM was lowest among patients younger than 50 years. We observed an increase in RCC-SM until the age of 50, at which point the level of risk reached a plateau. We observed a second increase among patients aged 75-89 years. We found similar patterns when we stratified patients according to the 2002 American Joint Committee on Cancer (AJCC) stages. CONCLUSION: The effect of age shows prognostic significance and indicates that follow-up and possibly secondary treatments might need to be adjusted according to the age of the patient.
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 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) | 0.001 | 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 teacher head, 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".