Age specific trends in prostate cancer tests, incidence and mortality in Australia since the introduction of the Prostate Specific Antigen (PSA) test
Bibliographic record
Abstract
Abstract Background Population trends in PSA screening and prostate cancer incidence do not perfectly correspond. We aimed to better understand relationships between trends in PSA screening, prostate cancer incidence and mortality in Australia. Methods Description of age standardised time trends in PSA tests, prostate biopsies, cancer incidence and mortality within Australia for the age groups: 45-74, 75-84, and 85+ years. Results PSA testing increased from its introduction in 1989 to a peak in 2008. It then declined in men aged 45-84 years. Prostate biopsies and cancer incidence declined from 1995 to 2000, in parallel with decrease in trans-urethral resections of prostate (TURP). After 2000, changes in biopsies and cancer incidence paralleled PSA screening in men 45-84 years, while in men ≥85 years, biopsies stabilised and incidence declined. More recently a reduction in TURP correlated with increased Dutasteride and Tamsulosin usage. Prostate cancer mortality in men aged 45-74 years remained low throughout. Mortality in men 75-84 years gradually increased until the mid 1990s, then gradually decreased. Mortality in men ≥85 years increased until the mid 1990s, then stabilised. Conclusions Age specific prostate cancer incidence largely mirrors PSA screening rates. Most deviation may be explained by changes in management of benign prostatic disease and incidental cancer detection. The timing of the small mortality reduction in men 75-84 years is more consistent with benefits from advances in treatment than with early detection through PSA. The large increases in prostate cancer incidence with minimal changes in mortality suggest overdiagnosis.
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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 | 0.008 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.007 |
| 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".