Editorial: Hereditary Breast and Ovarian Cancer: Current Concepts of Prevention and Treatment
Bibliographic record
Abstract
Breast cancer is the leading cause of cancer-related death in women worldwide. Whilst ovarian cancer is less common, it remains challenging due to late detection and high mortality (1). Most cases are considered sporadic; however, both tumor types may occur in patients with inherited mutations in cancer susceptibility genes (2). Hereditary breast and ovarian cancer syndrome (HBOC) accounts for 90% of the hereditary neoplasms and is predominantly associated with germline mutations in BRCA1 or BRCA2 genes (3). The mean cumulative risk of breast cancer is 57% in BRCA1 mutation carriers and 49% in BRCA2 mutation carriers, while ovarian cancer risk in women with BRCA1 and BRCA2 mutations is 40% and 18%, respectively. HBOC can also increase the risk, albeit to a lesser extent, for other neoplasms such as prostate or pancreatic cancer and malignant melanoma (3). Since the discovery of the BRCA genes and the development of clinical testing, the health advantages of identifying individuals at risk for HBOC have been well documented leading to the investigation and implementation of genetic counseling and screening, enhanced surveillance as well as surgical and non-surgical risk reduction options. We hope that a collection of review (including systematic review), mini-review, perspective and original research articles in this Research Topic will provide further insight on HBOC, including clinicopathologic features of associated cancers, genetic testing and treatment modalities, and their impact on patient outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".