Use of exogenous hormones in those at increased risk for breast cancer: contraceptive and menopausal hormones in gene carriers and other high-risk patients
Bibliographic record
Abstract
IMPORTANCE AND OBJECTIVE: Addressing the hormonal needs of individuals at increased risk of breast cancer (BC) can be a challenge. Observational, prospective, and case-control data support the safety of hormonal contraception in women, often with the added benefits of ovarian and endometrial cancer risk reduction. The majority of data on menopausal hormone therapy (HT) in the highest-risk patients comes from studies of patients with pathogenic variants in BRCA1 and BRCA2 who undergo early surgical menopause. The benefits of risk-reducing salpingo-oophorectomy are not minimized by HT, whereas its use mitigates accelerated osteoporosis and cardiovascular disease. In other patients at increased risk, such as with family history, studies have shown little risk with significant benefit. METHODS: We review evidence to help women's health practitioners aid patients in making choices. The paper is divided into four parts: 1, contraception in the very high-risk patient (ie, with a highly penetrant BC predisposition gene); 2, contraception in other patients at increased risk; 3, menopausal HT in the gene carrier; and 4, HT in other high-risk patients. DISCUSSION AND CONCLUSION: Women at increased risk for BC both early and later in life should be offered reassurance around the use of premenopausal and postmenopausal hormone therapies. The absolute risks associated with these therapies are low, even in the very high-risk patient, and the benefits are often substantial. Shared decision making is key in presenting options, and knowledge of the data in this area is fundamental to these discussions.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".