International trends in the uptake of cancer risk reduction strategies in women with a BRCA1 or BRCA2 mutation
Bibliographic record
Abstract
BACKGROUND: Women with a BRCA1 or BRCA2 mutation face high risks of breast and ovarian cancer. In the current study, we report on uptake of cancer screening and risk-reduction options in a cohort of BRCA mutation carriers from ten countries over two time periods (1995 to 2008 and 2009 to 2017). METHODS: Eligible subjects were identified from an international database of female BRCA mutation carriers and included women from 59 centres from ten countries. Subjects completed a questionnaire at the time of genetic testing, which included past use of cancer prevention options and screening tests. Biennial follow-up questionnaires were administered. RESULTS: Six-thousand two-hundred and twenty-three women were followed for a mean of 7.5 years. The mean age at last follow-up was 52.1 years (27-96 years) and 42.3% of the women had a prior diagnosis of breast cancer. In all, 27.8% had a prophylactic bilateral mastectomy and 64.7% had a BSO. Screening with breast MRI increased from 70% before 2009 to 81% at or after 2009. There were significant differences in uptake of all options by country. CONCLUSION: For women who received genetic testing more recently, uptake of prophylactic mastectomy and breast MRI is significantly higher than those who received genetic testing more than 10 years ago. However, uptake of both BSO and breast MRI is not optimal, and interventions to increase uptake are needed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".