Use of microsimulation modeling for research in breast cancer screening
Bibliographic record
Abstract
Prospective clinical trials on breast cancer screening take many years to provide results and are very costly. It is simply not feasible to answer all important questions by conducting a study. In addition, there are many inter-related variables that can affect screening outcomes and it is often not possible to study these individually through trials. Microsimulation modeling provides a practical alternative which allows the estimates of different key outcomes of screening in response to changes in the underlying human and technical variables. OncoSim Breast is part of a suite of specialized cancer microsimulation models developed by Statistics Canada in collaboration with The Canadian Partnership Against Cancer. The model simulates a cohort of women from birth to death through individual histories. At its heart is a mathematical function describing tumor growth. As women progress through life, at each time point, calculations are performed through random number selection, weighted by empirical probability data for each phenomenon in the simulation. The model is adapted to a particular problem by creating “scenarios” specifying the assumptions regarding the members of the cohort and any screening intervention(s) and treatment. We demonstrate how it can be helpful in optimizing screening regimens, predicting the impact of technical innovations and improvements and studying other problems where trials would be difficult or impossible to perform.
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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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".