Breast cancer risk predictions by birth cohort and ethnicity in a population-based screening mammography program
Bibliographic record
Abstract
OBJECTIVES: To examine whether birth cohorts affect the risk of breast cancer for East Asian, First Nations, African, South Asian and Caucasian ethnicities in British Columbia (BC). METHODS: We used Cox PH models adjusted for well-known risk factors, such as age, breast density, mammographic features on false positives, and family history, to examine risk of breast cancer among East Asian, First Nations, African and South Asian ethnicities, relative to Caucasian, across three birth cohorts. RESULTS: and invasive breast cancer diagnoses. East Asians screened in BC were found to have a lower risk of breast cancer in the birth cohort born pre-1946 compared to Caucasian, but there was no statistically significant decrease for East Asians born after 1946. First Nations had an increased risk of breast cancer compared with Caucasian for all birth cohorts ranging from 1.1 to 2.0x the risk, which was statistically significant for those born after 1965. South Asians showed a statistically significant decrease in risk ranging from 0.58 to 0.81x lower compared with Caucasians for all birth cohorts. CONCLUSION: Risk of breast cancer for South Asians living in BC was found to be lower than Caucasians for each birth cohort examined, while East Asians had a comparable risk of breast cancer, First Nations had a consistently higher risk than Caucasians. ADVANCES IN KNOWLEDGE: When accounting for birth cohort, compared to Caucasians, South Asians have a decreased risk, First Nations have an increased risk, and East Asians have a similar risk of breast cancer.
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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 | 0.001 |
| 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".