Canadian cancer risk management model: A new health policy tool useful in policy decisions related to lung cancer.
Bibliographic record
Abstract
e16541 Background: Fiscal constraint is forcing decision-makers to make choices about which cancer control initiatives to fund in publically funded health care systems. A Cancer Risk Management (CRM) model has been developed for the Canadian Partnership Against Cancer as a web enabled platform to support health policy decision-makers. Methods: The CRM uses dynamic, longitudinal microsimulation techniques to simulate and project realistic, representative populations. Each disease-specific module interacts within a single framework that incorporates Canadian demographic characteristics (births, mortality, immigration, emigration, interprovincial migration), educational status, risk factors (smoking, radon exposure, other as appropriate to the disease being modelled) and economic factors (earnings, taxes, government transfers). CRM utilizes current data lung cancer (LC) and colorectal cancer incidence, disease management and case fatality in Canada to assess impacts on population health and the cost to the health care system. Data sources include large national surveys, cancer registries and census data, as well as medical literature and expert opinion. Results: The CRM has simulated the impact of prevention strategies for LC, adjuvant therapy for resected LC and new therapies for advanced disease. For example, a 50% reduction in current smoking prevalence could result in a 10% reduction in lung cancer incident cases at 10 years and result in a cumulative direct health care cost saving of $215 M in Canada. The recent identification of a 20% mortality reduction using low dose computerized tomographic screening in high-risk individuals will be modeled using this platform once the full publication of results is available, including details of the frequency of investigation for false positives and the interventions utilized to investigate these cases. Conclusions: The CRM will likely become a key resource to Canadian decision-makers as fiscal constraints and new screening and treatment approaches increasingly come into conflict with each other.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.002 |
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".