BRCA Mutation Testing for First-Degree Relatives of Women With High-Grade Serous Ovarian Cancer [4OP]
Bibliographic record
Abstract
INTRODUCTION: Women with high-grade serous ovarian cancer (HGSC) have a 20% chance of carrying a BRCA1 or BRCA2 mutation and are eligible for BRCA testing. Many are untested, therefore their female first-degree relatives (FDR) may not qualify for testing unless they have specific ethnicity or other personal/family cancer history. We conducted a cost-effectiveness analysis to evaluate BRCA mutation testing for these FDR who are otherwise ineligible for testing. METHODS: A Markov Monte Carlo simulation model estimated the costs and benefits of 3 strategies for female FDR of HGSC patients whose BRCA status is unknown: 1) no BRCA testing; 2) universal BRCA testing, followed by risk-reducing bilateral salpingo-oophorectomy (RRBSO) for mutation carriers (“BRCA testing”); 3) universal RRBSO, without BRCA testing (“RRBSO”). Effectiveness was estimated in quality-adjusted life year (QALY) gains. Sensitivity analyses accounted for uncertainty around various parameters. The time horizon was 50 years. RESULTS: BRCA testing for female FDR of HGSC patients yielded a higher average QALY gain at acceptable cost compared to no BRCA testing, with an ICER of $7,729 per QALY. BRCA testing was more effective and less costly than RRBSO (19.20 QALYs vs 18.48 QALYs, and $10,108.35 vs $13,959.20, respectively), therefore BRCA testing is the dominant strategy. Results were stable over a wide range of plausible costs and estimates. Compliance with hormone replacement therapy had to exceed 84% for RRBSO to be the preferred strategy. CONCLUSION: BRCA mutation testing should be offered to all female first-degree relatives of women with high-grade serous ovarian cancer when their BRCA mutation status is unknown.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".