Women's interest in genetic testing for breast cancer risk: the influence of sociodemographics and knowledge.
Bibliographic record
Abstract
The objective of this study was to assess women's interest in genetic testing for breast cancer risk. Randomly selected samples of 761 women without breast cancer from the general population of British Columbia, Canada, and 260 women with breast cancer from the provincial cancer registry participated in a telephone survey that assessed interest in genetic testing for breast cancer risk, knowledge of hereditary breast cancer and genetic testing, and sociodemographics. Women with breast cancer did not possess superior knowledge of breast cancer genetics compared with women from the general population. Of the women with breast cancer, 30.8% reported interest in testing or had been tested, compared with 28.5% of women without breast cancer. Controlling for differences in age, education, personal history of breast cancer, and knowledge of genetics, women with at least one relative with breast cancer were 2.3 times more likely to express interest in genetic testing for breast cancer risk than those with no family history. There were significant interactions between breast cancer status and education and between age and knowledge of breast cancer genetics. Women without breast cancer and with a positive family history, who were between 20 and 40 years of age, were most likely to be interested in testing. The women with breast cancer who were interested in testing tended to be approximately 50 years of age, had a positive family history, and had more years of education. Women with a family history of breast cancer, well-educated women with breast cancer, and younger women, particularly those with knowledge of genetic testing, are important target audiences for community-based education on genetic testing for breast cancer risk.
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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 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".