Clinical outcomes of modelling mammography screening strategies.
Bibliographic record
Abstract
BACKGROUND: A validated breast cancer model can be used to compare health outcomes associated with different screening strategies. DATA AND METHODS: The University of Wisconsin Cancer Intervention and Surveillance Modeling Network (CISNET) breast cancer microsimulation model was adapted to simulate breast cancer incidence, screening performance and delivery of optimal therapies in Canada. The model considered effects of breast density on incidence and screening performance. Model predictions of incidence, mortality and life-years (LY) gained for a 1960 birth cohort of women for No Screening were compared with 11 digital mammography screening strategies that varied by starting and stopping age and frequency. RESULTS: In the absence of screening, the estimate of LYs lost from breast cancer was 360.1 per 1,000 women, and each woman diagnosed with breast cancer after age 40 who dies of breast cancer would lose an average of 19.1 years. Biennial screening at ages 50 to 74 resulted in 116.3 LYs saved. Annual screening at ages 40 to 49, followed by biennial screening to age 74, resulted in 170.3 LY saved. Screening annually at ages 40 to 74 recovered the most: 214 LY saved. Annual screening at age 40 resulted in 54 LY gained per 1,000 women. More frequent screening was associated with an increased ratio of detection of ductal in situ to invasive cancers, more abnormal recalls and more negative biopsies, but a reduction in the number of women required to be screened per life saved or per LY saved. INTERPRETATION: In general, mortality reduction was found to be associated with the total number of lifetime screens for breast cancer. However, for the same number of screens, more frequent screening after age 50 appeared to have a greater impact than beginning screening earlier. When the number of LYs saved by screening was considered, a greater impact was achieved by screening women in their 40s than by reducing the interval between screens.
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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 | 0.000 |
| 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".