Shooting for the Moon: Can We Cut Cancer Mortality in Canada By 50% By 2050?
Bibliographic record
Abstract
INTRODUCTION: The United States of America reignited their Cancer Moonshot Initiative in 2022 with an ambitious goal to reduce cancer mortality by 50% over the next 25 years. In this study, we estimated how and whether a similar cancer control initiative could be achieved in Canada. METHODS: We used the OncoSim microsimulation suite to address three questions: (1) what is the expected mortality from cancer in Canada by 2050 given the current trends?; (2) what would be the maximal impact on reducing cancer mortality with prevention and increased screening activities? and, (3) if a 50% reduction in projected cancer mortality could not be achieved through the primary and secondary intervention efforts, what additional advancements and discoveries would be needed to fill the "lunar gap"? We modeled the joint impact of risk-factor reduction and screening, as well as the independent effects of prevention and screening alone, on projected cancer mortality. RESULTS: Our models suggest that there will be an expected 133,395 cancer deaths in 2050 in Canada. Approximately 33% of these cancer deaths could be prevented by risk-factor reduction and increased screening programs by the year 2050. This would leave a "lunar gap" of about 16%-17% that would need to be bridged with novel discoveries in cancer risk prevention, early detection, and treatment. CONCLUSION: While current knowledge and implementation of prevention and screening would have a considerable impact on a Canadian cancer moonshot, additional efforts are needed to implement cancer control initiatives and fuel additional discoveries to fill the gap.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".