Cancer Incidence in a Population of Jewish Women at Risk of Ovarian Cancer
Bibliographic record
Abstract
PURPOSE: To evaluate the incidence and clinical characteristics of ovarian and other cancers in a cohort of women at risk of developing ovarian cancer. PATIENTS AND METHODS: The Gilda Radner Ovarian Cancer Detection Program in Los Angeles, CA, was established in 1991 to study the efficacy of screening in the early detection of ovarian cancer. We present findings from a historical cohort of 290 Jewish women who were offered BRCA testing for three common founder mutations (BRCA1 185delAG and 5382insC and BRCA2 6174delT). RESULTS: In 10 years, 17 cancers were observed (1,111 per 100,000 per year), including six breast and eight ovarian or related cancers. A high proportion of cancers of peritoneal origin was observed. The majority (86%) of women with incident breast or ovarian/peritoneal cancer carried a mutation in the BRCA1 gene. The overall cancer incidence among carriers of mutations in the BRCA1 gene was estimated to be 5,450 per 100,000 per year, corresponding to a cumulative incidence of 47.5% at 10 years. In contrast, the cumulative incidence of cancer among noncarriers was 2.5% (P < 10(-8)). After adjustment for sampling, the risks to BRCA1 mutation carriers at 10 years were estimated to be 21% for ovarian/peritoneal/tubal cancer, 16% for breast cancer, and 36% for all cancers. CONCLUSION: The excess risk of breast and ovarian cancer in Jewish women with a family history of ovarian cancer is largely attributable to mutations in BRCA1. Intensive surveillance by use of CA-125 and ultrasound does not seem to be an effective means of diagnosing early-stage ovarian cancer in this high-risk cohort.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".