The International BRCA1 /2Carrier Cohort Study: Purpose, Rationale, and Study Design
Bibliographic record
Abstract
Breast cancer is the most common cancer affecting women in developed countries. Efforts to decrease breast cancer mortality have focused on early detection and improved treatment. Identification of women at particularly high risk of developing the disease provide a group on whom expensive and rigorous screening programs are cost-effective and a cohort of women who may benefit from trials of chemopreventive agents. The discovery of at least two genes which, when altered, confer markedly increased susceptibility to breast cancer has facilitated the identification of a cohort of individuals at particularly high risk of this disease. Although counseling and genetic testing of at-risk women is now underway in many centers in Europe and North America, the risks of cancer provided to gene carriers is based on limited data obtained from retrospective analyses in families largely selected for high incidence of living affected women. The same limitations apply to our knowledge of the risks of cancer at other sites and the possible interactions of these mutations with other genes and environmental effects (Phelan et al . 1996; Rebbeck et al . 1999; Brunet et al . 1998). These studies estimate a risk of breast cancer in female carriers of ~ 50% by age 50 and a ~ 80% lifetime risk (Easton et al . 1993; Easton et al . 1995; Ford et al . 1998). These two genes also predispose to an approximate 40% risk of ovarian cancer as well as 3 to 4-fold increased risks of colon ( BRCA1 ) and pancreatic cancer ( BRCA2 ) in both male and female carriers and prostate cancer ( BRCA1 and BRCA2 ) in men (Ford et al . 1994; Breast Cancer Linkage Consortium 1999). There have also been published reports that reproductive or other factors may alter the baseline risks conferred by BRCA1 alterations (Goldgar et al . 1995; Narod et al . 1995). Although prophylactic surgery is sometimes used as a means of prevention, there is little or no data regarding the efficacy of these procedures. While several countries have begun national or regional efforts to address these issues prospectively, the sample sizes required for adequate precision of risk estimates and power for detection of risk modification are often beyond the scope of any one center. Accordingly, there is a clear need for international coordination of these efforts with a standardized enrollment and follow-up protocol, and for combined data analyses.
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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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".