Global Disparities in Breast Cancer Genetics Testing, Counselling and Management
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Hereditary breast cancers, mainly due to BRCA1 and BRCA2 mutations, account for only 5-10% of this disease. The threshold for genetic testing is a 10% likelihood of detecting a mutation, as determined by validated models such as BOADICEA and Manchester Scoring System. A 90-95% reduction in breast cancer risk can be achieved with bilateral risk-reducing mastectomy in unaffected BRCA mutation carriers. In patients with BRCA-associated breast cancer, there is a 40% risk of contralateral breast cancer and hence risk-reducing contralateral mastectomy is recommended, which can be performed simultaneously with surgery for unilateral breast cancer. Other options for risk management include surveillance by mammogram and breast magnetic resonance imaging, and chemoprevention with hormonal agents. With the advent of next-generation sequencing and development of multigene panel testing, the cost and time taken for genetic testing have reduced, making it possible for treatment-focused genetic testing. There are also drugs such as the PARP inhibitors that specifically target the BRCA mutation. Risk management multidisciplinary clinics are designed to quantify risk, and offer advice on preventative strategies. However, such services are only possible in high-income settings. In low-resource settings, the prohibitive cost of testing and the lack of genetic counsellors are major barriers to setting up a breast cancer genetics service. Family history is often not well documented because of the stigma associated with cancer. Breast cancer genetics services remain an unmet need in low- and middle-income countries, where the priority is to optimise access to quality treatment.
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.
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 | 0.000 | 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 it