Bibliographic record
Abstract
Abstract Women who inherit a pathogenic variant (mutation hereafter) in the BRCA1 or BRCA2 geneface extremely high lifetime risks of developing breast and ovarian (or fallopian tube) cancer. More than two decades since the discovery of these genes, and primary prevention with bilateral mastectomy and salpingo-oophorectomy remain the most effective options to manage cancer risk in this population. Understanding the impact of exogenous hormone use is important for both the clinical management of high-risk women and for furthering our knowledge of the pathogenesis of BRCA-associated disease. In this session, I will review the current epidemiologic data surrounding the role of exogenous (anti)hormone use on BRCA-cancer risk. Specifically, I will discuss the role of tamoxifen in preventing BRCA-associated breast cancer and I will describe whether use of oral contraceptives or hormone replacement therapy (HRT) increase the risk of breast cancer. Where possible, I will present data by gene mutation. Potential associations with the risk of ovarian cancer will also be referred to, given that managing BRCA-cancer risks is a balancing act. Finally, I will review how the epidemiologic information has contributed to the discovery of novel targets for the non-surgical prevention of BRCA1-associated breast cancer and gaps in the literature to be addressed in future research. Citation Format: Joanne Kotsopoulos. Hormonal exposure and risk in BRCA1/2 carriers [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr ED2-3.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".