Bibliographic record
Abstract
It has been 10 years since the BRCA1 gene was first identified. During this decade, genetic testing for breast cancer susceptibility has been incorporated into the practice of oncology. In this process, the identification of families at the highest hereditary risk for cancer has served as a model to test strategies for prevention or early detection of breast malignancies. An emerging literature has explored primary prevention through risk reducing surgery and chemoprevention, as well as secondary prevention utilizing such approaches as magnetic resonance imaging (MRI) to achieve early detection of breast cancer in women with BRCA1 or BRCA2 mutations. Tailored treatments are being explored for newly diagnosed women with BRCA mutations. Ultimately, individual risk estimates and clinical management plans will be generated for women carrying BRCA mutations, based on consideration of the particular mutation inherited and also on the presence of modifying genetic and environmental factors. Both BRCA1 and BRCA2 are involved in the cellular response to DNA damage and interact with other proteins involved in double-stranded DNA repair. The effects of inherited mutations in these genes are similar, and mutations of both types predispose carriers to female and male breast cancer, and to ovarian cancer. The risk of male breast cancer is higher in BRCA2 carriers; ovarian cancer risk is higher in those carrying BRCA1 mutations. In addition, BRCA2 mutations appear to predispose both men and women to a wide range of other cancer types. The reasons for these tissue-specific differences between the two genes is not clear.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".