Missed therapeutic and prevention opportunities in women with BRCA‐mutated epithelial ovarian cancer and their families due to low referral rates for genetic counseling and BRCA testing: A review of the literature
Bibliographic record
Abstract
Answer questions and earn CME/CNE Fifteen percent of women with epithelial ovarian cancer have inherited mutations in the BRCA breast cancer susceptibility genes. Knowledge of her BRCA status has value both for the woman and for her family. A therapeutic benefit exists for the woman with cancer, because a new family of oral drugs, the poly ADP-ribose polymerase (PARP) inhibitors, has recently been approved, and these drugs have the greatest efficacy in women who carry the mutation. For her family, there is the potential to prevent ovarian cancer in those carrying the mutation by using risk-reducing surgery. Such surgery significantly reduces the chance of developing this, for the most part, incurable cancer. Despite these potential benefits, referral rates for genetic counseling and subsequent BRCA testing are low, ranging from 10% to 30%, indicating that these therapeutic and prevention opportunities are being missed. The authors have reviewed the relevant available literature. Topics discussed are BRCA and its relation to ovarian cancer, the rates of referral for genetic counseling/BRCA testing, reasons for these low rates, potential strategies to improve on those rates, lack of effectiveness of current screening strategies, the pros and cons of risk-reducing surgery, other prevention options, and the role and value of PARP inhibitors. CA Cancer J Clin 2017;67:493-506. © 2017 American Cancer Society.
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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".