When “One Size Fits All” Fits None: A Commentary on the Impacts of the“Draft Canadian Breast Cancer Screening Guidelines” on Racialized Populations in Canada
Bibliographic record
Abstract
Epidemiological data show racial and ethnic differences exist in breast cancer morbidity and mortality amongst Black, Indigenous, Asian, and Hispanic populations, with non-white females experiencing earlier age at diagnosis, more aggressive breast cancer subtypes and advanced cancer stages, and earlier mortality than white females. However, the current Canadian breast cancer screening guidelines recommend biannual screening for all females starting from age 50 to age 74 and suggest not to screen individuals aged 40-49. In May 2024, the Canadian Task Force for Preventative Health released updated draft breast cancer screening guidelines, maintaining such recommendations for screening. Both the existing and the proposed guidelines fail to account for the unique cancer burden amongst racialized populations in Canada and risk further perpetuation of existing racial and ethnic disparities by underscreening racialized females. This commentary will present data regarding racial disparities in cancer burden, highlighting the role social and biological factors play in impacting cancer risk and age of disease and presenting perspectives from stakeholder groups reflecting the impacts of current screening guidelines. Ultimately, we critique the current "one-size-fits-all" approach to breast cancer screening in Canada, emphasizing the need for adapted screening practices with the understanding that the current approaches overlook the needs of racialized Canadian populations.
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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 | 0.019 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.007 | 0.002 |
| Research integrity | 0.020 | 0.026 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".