Comparing Canada's OncoSim-Breast model with the United States' Cancer Intervention and Surveillance Modeling Network (CISNET) breast cancer models
Bibliographic record
Abstract
Background: The OncoSim-Breast model, developed by the Canadian Partnership Against Cancer and Statistics Canada, represents breast cancer-related events in the Canadian female population. This study aimed to compare OncoSim-Breast with recent results from the United States' National Cancer Institute's Cancer Intervention and Surveillance Modeling Network (CISNET) breast cancer models. The primary focus was on the impact of extending breast cancer screening to women aged 40 to 49. Data and methods: The OncoSim-Breast model used Canadian demographics, competing mortality, and test performance, while the CISNET models used comparable United States data to analyze 10 different mammography screening scenarios. Lifetime outcomes were calculated for a cohort of 40-year-old women born in 1980, assuming perfect adherence to digital mammography screening. OncoSim-Breast's estimates were compared with the median and range of estimates from the five CISNET models. The primary outcomes were breast cancer deaths averted and life years gained per 1,000 40-year-old women. Results: OncoSim-Breast projected that starting screening at age 40 would lead to 1.7 breast cancer deaths averted and 53 life years gained per 1,000 women, compared with starting screening at age 50. CISNET models projected a median of 1.3 breast cancer deaths averted (range 0.8 to 3.2) and 43 life years gained (range 31 to 103) per 1,000 women for the same scenario. Secondary outcomes estimated by OncoSim-Breast and CISNET models were similarly consistent and comparable. Interpretation: This study demonstrates that OncoSim-Breast's estimates of the impact of starting breast cancer screening earlier align with those from CISNET models.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 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".