An examination of prostate cancer trends in Australia, England, Canada and USA: Is the Australian death rate too high?
Bibliographic record
Abstract
PURPOSE: To compare prostate cancer incidence and mortality rates in Australia, USA, Canada and England and quantify the gap between observed prostate cancer deaths in Australia and expected deaths, using US mortality rates. METHODS: Analysis of age-standardised prostate cancer incidence and mortality rates, using routinely available data, in four similarly developed countries and joinpoint regression to quantify the changing rates (annual percentage change: APC) and test statistical significance. Expected prostate cancer deaths, using US mortality rates, were calculated and compared with observed deaths in Australia (1994-2010). RESULTS: In all four countries, incidence rates initially peaked between 1992 and 1994, but a second, higher peak occurred in Australia in 2009 (188.9/100,000), rising at a rate of 5.8 % (1998-2008). Mortality rates in the USA (APC: -2.9 %; 2004-2010), Canada (APC: -2.9 %; 2006-2011) and England (APC: -2.6 %; 2003-2008) decreased at a faster rate compared with Australia (APC: -1.7 %; 1997-2011). In 2010, mortality rates were highest in England and Australia (23.8/100,000 in both countries). The mortality gap between Australia and USA grew from 1994 to 2010, with a total of 10,895 excess prostate cancer deaths in Australia compared with US rates over 17 preceding years. CONCLUSIONS: Prostate cancer incidence rates are likely heavily influenced by prostate-specific antigen testing, but the fall in mortality occurred too soon to be solely a result of testing. Greater emphasis should be placed on addressing system-wide differences in the management of prostate cancer to reduce the number of men dying from this disease.
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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) | 0.000 | 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) | 0.000 | 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".