Prophylactic Interventions for Hereditary Breast and Ovarian Cancer Risks and Mortality in BRCA1/2 Carriers
Bibliographic record
Abstract
BACKGROUND: Hereditary breast and ovarian cancers (HBOCs) pose significant health risks worldwide and are mitigated by prophylactic interventions. However, a meta-analysis of their efficacy and the impact of different genetic variants on their effectiveness is lacking. METHODS: A systematic review and meta-analysis were conducted, adhering to Cochrane guidelines. The review encompassed studies that involved prophylactic interventions for healthy women with BRCA variants, focusing on cancer incidence and mortality outcomes. The Newcastle-Ottawa Scale was used for risk of bias assessment. We pooled the extracted outcomes using random effects models and conducted subgroup analyses stratified by intervention, variant, and cancer types. RESULTS: A total of 21 studies met the inclusion criteria. The meta-analysis revealed that prophylactic interventions significantly reduced cancer risk and mortality. The subgroup analysis showed a greater protective effect for BRCA2 than BRCA1 variant carriers. Risk-reducing surgeries (RRS) were more effective than chemoprevention, with RRS notably reducing cancer risk by 56% compared to 39% for chemoprevention. Prophylactic oophorectomy significantly reduced HBOC risks, while the effect of prophylactic mastectomy and chemoprevention on mortality was less conclusive. CONCLUSIONS: Prophylactic interventions significantly reduce the risk of HBOC and associated mortality. This comprehensive analysis provides insights for future economic evaluations and clinical decision-making in HBOC interventions.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.010 |
| 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 | 0.001 |
| 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".