Bibliographic record
Abstract
The medical histories of breast cancer-prone families have been described for over a century. The pattern of breast cancer occurrences in these families is most consistent with an autosomal dominant mode of inheritance. The location of a gene that could explain the pattern of transmission of the breast cancer trait in families averaging early (pre-menopausal) onset of breast cancer was reported in 1990. Since then, two genes have been identified: BRCA1 and BRCA2. Germ-line mutations in these two genes confer susceptibility to breast (female and male) and ovarian cancer, and account for a significant proportion of hereditary breast cancer in two cancer syndromes: site-specific breast cancer and the breast-ovarian cancer syndrome. Other hereditary syndromes that feature breast cancer are Li-Fraumeni syndrome, Cowden disease, and ataxia telangiectasia, whose carriers have been shown to harbor germ-line mutations in TP53, PTEN, and ATM, respectively. There may be other genetic factors that contribute to hereditary breast cancer, since not all families with multiple cases of breast cancer harbor germ-line BRCA1 or BRCA2 mutations. Host factors (such as lifestyle choices) and other genes may modulate risk of breast cancer in mutation carriers.
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".