Modeling breast cancer progression and evaluating screening policies
Bibliographic record
Abstract
In this paper we use a five-state model to describe the progression of invasive breast cancer. The states of the model are: 1. Healthy or non-detectable cancer, 2. Preclinical (screening detectable cancer), 3. Clinical (symptoms are evident), 4. Death due to breast cancer, and 5. Death due to causes other than breast cancer. We model the natural progression of breast cancer from healthy state to clinical cancer using a partially observable Markov model. We model the survival time from cancer diagnosis to breast cancer mortality using a Weibull Proportional Hazards Model (PHM). The effect of covariates in both models are also studied. We then combine the two models and develop a simulation model to evaluate the effect of different screening intervals in reducing breast cancer mortality. We use the data from the Canadian National Breast Screening Study (CNBSS), which consists of two randomized screening trials designed to evaluate the effect of mammography on women aged 40-59. The results reveal that screening can be effective in detecting breast cancer at earlier stages, so reducing breast cancer mortality. We estimated a higher reduction for older women.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".