The OncoSim-Breast cancer microsimulation model
Bibliographic record
Abstract
ABSTRACT Background The increasing demand for health care resources requires measures to evaluate the impact of cancer control approaches. A cancer simulation model can help integrate new knowledge to inform clinical and policy decisions. OncoSim-Breast is a breast cancer simulation model. This paper aims to describe the key assumptions in the OncoSim-Breast model and how well it reproduces more recent breast cancer trends and the observed effects in a randomized screening trial. Methods The OncoSim-Breast model simulates the onset, growth and spread of invasive and ductal carcinoma in situ tumours. The model combines Canadian cancer incidence, mortality, screening program and cost data to project population-level outcomes. Users can change the model input to answer specific policy questions. Here we report three validation exercises. First, we compared the model’s projected breast cancer incidence and stage distributions with the observed data in the Canadian Cancer Registry. Second, we compared OncoSim’s projected breast cancer mortality with the Vital Statistics. Third, we replicated the UK Age trial to compare the model’s projections with the trial’s observed screening effects. Results OncoSim-Breast’s projected incidence, mortality and stage distribution of breast cancer were close to the observed data in the Canadian Cancer Registry and the Vital Statistics. OncoSim-Breast also reproduced the breast cancer screening effects observed in the UK Age trial. Interpretation OncoSim-Breast’s ability to reproduce the observed population-level breast cancer trends and the screening effects in a randomized trial increases the confidence of using its results to inform policy decisions related to early detection of breast cancer.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".