Prostate Cancer Incidence and Mortality Rates and Trends in the United States and Canada
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to compare prostate cancer incidence and mortality trends between the United States and Canada over a period of approximately 30 years. METHODS: Prostate cancer incident cases were chosen from the National Cancer Institute's Surveillance Epidemiology and End Results (SEER) Program to estimate rates for the United States white males and from the Canadian Cancer Registry for Canadian men. National vital statistics data were used for prostate cancer mortality rates for both countries, and age-adjusted and age-specific incidence and mortality rates were calculated. Joinpoint analysis was used to identify significant changes in trends over time. RESULTS: Canada and the U.S. experienced 3.0% and 2.5% growth in age-adjusted incidence from 1969-90 and 1973-85, respectively. U.S. rates accelerated in the mid- to late 1980s. Similar patterns occurred in Canada with a one-year lag. Annual age-adjusted mortality rates in Canada were increasing 1.4% per year from 1977-93 then fell 2.7% per year from 1993-99. In the U.S., annual age-adjusted mortality rates for white males increased 0.7% from 1969-1987 and 3.0% from 1987-91, then decreased 1.2% and 4.5% during the 1991-94 and 1994-99 periods, respectively. CONCLUSIONS: Recent incidence patterns observed between the U.S. and Canada suggest a strong relationship to prostate-specific antigen (PSA) test use. Clinical trials are required to determine any effects of PSA test use on prostate cancer and overall mortality.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".