Hereditary breast and ovarian cancer in Asia: genetic epidemiology ofBRCA1 andBRCA2
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
Ethnic differences in cancer incidence and mortality result from differences in genetic and epidemiologic risk factors. Mutations in BRCA1 and BRCA2 account for a small proportion of all breast cancer cases, but for a much higher proportion of cases with a strong family history of breast or ovarian cancer. Germline mutations in BRCA1 and BRCA2 have been identified in individuals of many races and ethnic groups and the frequency of mutations varies between these groups. Some of the differences in cancer risk between populations may be the result of founder mutations in these genes. The cost and time required for mutation analysis are reduced considerably when founder mutations are identified for a specific ethnic group. The BRCA2 999del5 mutation in Iceland and three BRCA mutations in Ashkenazi Jews are well characterized. However, considerably less is known about the contribution of mutations in the BRCA1 and BRCA2 genes outside of European groups. Studies conducted on the Asian populations described here have expanded our current knowledge of genetic susceptibility and its contribution to breast and ovarian cancer rates in Asian populations.
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 | 0.001 | 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