Contemporary incidence and mortality rates of kidney cancer in the United States
Bibliographic record
Abstract
INTRODUCTION: This is a timely update of incidence and mortality for renal cell carcinoma (RCC) in the United States. METHODS: Relying on the Surveillance, Epidemiology, and End Results (SEER) database, we computed age-adjusted incidence, mortality rates and 5-year cancer-specific survival (CSS) for patients with histologically confirmed kidney cancer between 1975 and 2009. Long-term (1975-2009) and short-term (2000-2009) trends were examined by joinpoint analysis, and quantified using the annual percent change (APC). The reported findings were stratified according to disease stage. RESULTS: Age-adjusted incidence rates of RCC increased by +2.76%/year between 1975 and 2009 (from 6.5 to 17.1/100 000 person-years, p < 0.001), and by +2.85%/year between 2000 and 2009 (p < 0.001). For the same time points, the corresponding APC for the incidence of localized stage were +4.55%/year (from 3.0 to 12.2/100 000 person years, p < 0.001), and +4.42%/year (p < 0.001), respectively. The incidence rates of regional stage increased by +0.88%/year between 1975 and 2009 (p < 0.001), but stabilized in recent years (2000-2009: +0.56%/year, p = 0.4). Incidence rates of distant stage remained unchanged in long- and short-term trends. Overall mortality rates increased by +1.72%/year between 1975 and 2009 (from 1.2 to 5.0/100 000 person-years, P<0.001), but stabilized between 1994 and 2004 (p = 0.1). Short-term mortality rates increased in a significant fashion by +3.14%/year only for localized stage (p < 0.001). INTERPRETATION: In contemporary years, there is a persisting upward trend in incidence and mortality of localized RCC.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".