Assessing the Breast Cancer Risk Distribution for Women Undergoing Screening in British Columbia
Bibliographic record
Abstract
Breast cancer risk estimations are both informative and useful at the population level, with many screening programs relying on these assessments to allocate resources such as breast MRI. This cross-sectional multicenter study attempts to quantify the breast cancer risk distribution for women between the ages of 40 to 79 years undergoing screening mammography in British Columbia (BC), Canada. The proportion of women at high breast cancer risk was estimated by surveying women enrolled in the Screening Mammography Program of British Columbia (SMPBC) for known breast cancer risk factors. Each respondent's 10-year risk was computed with both the Tyrer-Cuzick and Gail risk assessment models. The resulting risk distributions were evaluated using the guidelines from the National Institute for Health and Care Excellence (United Kingdom). Of the 4,266 women surveyed, 3.5% of women between the ages of 40 to 79 years were found to have a high 10-year risk of developing breast cancer using the Tyrer-Cuzick model (1.1% using the Gail model). When extrapolated to the screening population, it was estimated that 19,414 women in the SMPBC are considered to be at high breast cancer risk. These women may benefit from additional MRI screening; preliminary analysis suggests that 4 to 5 additional MRI machines would be required to screen these high-risk women. However, the use of different models and guidelines will modify the number of women qualifying for additional screening interventions, thus impacting the MRI resources required. The results of this project can now be used to inform decision-making groups about resource allocation for breast cancer screening in BC.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".