Environmental variables and family history of breast cancer
Bibliographic record
Abstract
1088 Background: Women with a family history are at twice the risk of developing breast cancer than are women in the general population. The family history might be caused by genetic or environmental variables, or it might occur by chance. Germline mutations in BRCA1 and BRCA2 account for about half of familial breast cancer cases. Some authors have proposed that there are other, yet unknown, forms of genetic susceptibility to explain many of the remaining family histories. Indeed, no one has produced evidence that a substantial fraction of family histories is due to a non-chance, non-genetic variable. Methods: We reviewed data from a large population-based case-control study of breast cancer in British Columbia (BC), Canada, and tested whether non-genetic variables predicted which women had a family history of the disease. Results: Our analysis included 658 pre-menopausal women (318 breast cancer cases and 340 healthy controls) and 1384 post-menopausal women (699 cases and 685 controls). About 11% of pre-menopausal women had a family history of breast cancer (16% of cases and 7% of controls); about 16% of post-menopausal women had a family history of breast cancer (20% of cases and 12% of controls). The post-menopausal controls with a family history were older on average than the post-menopausal cases (p=0.005) but otherwise there were no major differences in non-genetic factors between women with and without a family history. Interpretation: A family history of breast cancer might be due to genetic or environmental factors, but our analysis has not found any environmental factors that predict whether a woman has a family history of breast cancer.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.007 | 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".