Current Status of the Management of Hereditary Breast and Ovarian Cancer in Asia: First Report by the Asian BRCA Consortium
Bibliographic record
Abstract
BACKGROUND: BRCA1/BRCA2 mutations are associated with an increased lifetime risk for hereditary breast and ovarian cancer (HBOC). Compared with the Western developed countries, genetic testing and risk assessment for HBOC in Asia are less available, thus prohibiting the appropriate surveillance, clinical strategies and cancer management. METHODS: The current status of HBOC management in 14 Asian countries, including genetic counselling/testing uptakes and clinical management options, was reviewed. We analysed how economic factors, healthcare and legal frameworks, and cultural issues affect the genetic service availability in Asia. RESULTS: In 2012, only an estimated 4,000 breast cancer cases from 14 Asian countries have benefited from genetic services. Genetic testing costs and the absence of their adoption into national healthcare systems are the main economic barriers for approaching genetic services. Training programmes, regional accredited laboratories and healthcare professionals are not readily available in most of the studied countries. A lack of legal frameworks against genetic discrimination and a lack of public awareness of cancer risk assessment also provide challenges to HBOC management in Asia. CONCLUSIONS: The Asian BRCA Consortium reports the current disparities in genetic services for HBOC in Asia and urges the policy makers, healthcare sectors and researchers to address the limitations in HBOC management.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".