The OncoSim Cancer Simulation Platform: A Tool to Project the Population Effects of Cancer Control Interventions in Canada
Bibliographic record
Abstract
Background: Cancer control interventions exert their effects over multiple decades. To evaluate diverse and competing opportunities to reduce future cancer burden it is desirable to understand long-term effects prior to any new program implementation or significant change. Internationally, modeling is becoming an accepted source of planning information for decision-makers. Aim: We will describe the construction and use of the OncoSim microsimulation model, which was developed to evaluate cancer control strategies in Canada. Methods: OncoSim is a suite of models (cancers of the lung, colorectum, cervix and breast, plus a composite 32-cancer model) used to address key policy questions and support decision-making. It is led by the Canadian Partnership Against Cancer with model development by Statistics Canada. OncoSim incorporates risk factors, cancer natural history, screening, treatment, survival and end-of-life care. Wherever possible it is informed by Canadian data sources. Models are calibrated to reproduce a range of cancer-specific statistics, e.g., current and historical Canadian cancer-specific incidence and mortality, smoking patterns, and results of screening. The site-specific models have undergone further validation by replicating reported short-term effects of cancer prevention and screening interventions. Users may customize interventions through modifying input parameters. Outputs include incidence, mortality, costs, cost-effectiveness, and resource utilization. Users from the public sector have access at no cost to OncoSim and receive extensive support from a multidisciplinary technical team. The model is continually updated to incorporate emerging knowledge. Results: OncoSim has been used to support cancer control decision-making at the national and provincial/territorial levels. Applications include: national guidelines recommendations for colorectal and lung cancer screening; comparison of cytology vs. HPV based cervical cancer screening; and integration of smoking cessation into low-dose CT lung cancer screening. Conclusion: Validated simulation models such as OncoSim can be a versatile and efficient tool for cancer control planners to evaluate and prioritize cancer control strategies.
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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 | 0.001 | 0.001 |
| 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".