Assessment of other‐cause mortality in localized renal cell carcinoma patients within 15 years: A population‐based analysis
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Five-year other cause mortality (OCM) after nephrectomy for non-metastatic renal cell carcinoma (RCC) should be marginal in properly selected surgical candidates. We examined 5-year OCM rates as a quality of care indicator for patient selection. MATERIALS AND METHODS: Within the Surveillance, Epidemiology, and End Results database (1997-2011), we identified 59267 RCC patients treated with either radical (n = 27 804, 46.9%) or partial nephrectomy (n = 31 463, 53.1%). Temporal trends and multivariable Cox regression analyses assessed 5-year OCM. Data were stratified according to age group, year of diagnosis, race, marital status, gender, and socio-economic status. The overall OCM rates for the entire cohort at 5 years of follow-up was 4.7% and decreased from 9.4% to 5.6% over the study span (-3.8%, P < .001). The greatest decrease in 5-year OCM rates over time was recorded in patients >70 years (17.0%-9.6%, slope, -0.6%/y), as well as in African-Americans (12.0-6.2%; slope, -0.3%/y) and in males (8.9%-4.7%; slope, -0.3%, all P < .001). CONCLUSIONS: An important OCM decrease was recorded over the study span. Nonetheless, further improvement may be accomplished, especially in African-Americans, unmarried and older individuals, who exhibited higher OCM rates than others. These three groups may represent ideal targets for better patient selection based on OCM considerations.
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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 | 0.002 |
| 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 | 0.001 |
| 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".