Cancer control measures have prevented 230,000 deaths in Australia since the mid-1980s
Bibliographic record
Abstract
OBJECTIVE: To estimate the lives saved because of cancer control measures implemented in Australia, since age-standardised mortality rates (ASMRs) have been available. METHODS: Secondary data analysis using publicly available data. We estimated ASMRs using the (unadjusted) age-specific rates and the corresponding population counts. We also estimated the expected number of cancer deaths, the number of avoided cancer deaths and standardised mortality ratios (SMR). RESULTS: The overall ASMR for females peaked in 1985 at 116.50 per 100,000 (95% CI: 114.40, 118.60) and declined to 81.36 (95% CI: 80.24, 82.48) in 2018. For males, the overall ASMR peaked at 188.27 per 100,000 in 1987 (95% CI: 185.38, 191.16), declining to 116.08 per 100,000 (95% CI: 114.67, 117.48) in 2018. We estimated that 66,733 and 164,358 cancer deaths have been avoided for females and males, respectively. This corresponds to an 11% (SMR = 0.89, 95% CI: 0.89, 0.89) and 20% (SMR = 0.80, 95% CI: 0.80, 0.80) reduction in overall cancer mortality. CONCLUSIONS: When considering overall cancer rates, over 230,000 cancer-related deaths have been avoided in Australia since 1950. IMPLICATIONS FOR PUBLIC HEALTH: These estimates demonstrate the value of sustained cancer control investment, particularly in primary and secondary prevention.
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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 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".