Data from Modification of Ovarian Cancer Risk by <i>BRCA1/2</i>-Interacting Genes in a Multicenter Cohort of <i>BRCA1/2</i> Mutation Carriers
Bibliographic record
Abstract
Abstract Inherited BRCA1/2 mutations confer elevated ovarian cancer risk. Knowledge of factors that can improve ovarian cancer risk assessment in BRCA1/2 mutation carriers is important because no effective early detection for ovarian cancers exists. A cohort of 1,575 BRCA1 and 856 BRCA2 mutation carriers was used to evaluate haplotypes at ATM, BARD1, BRIP1, CTIP, MRE11, NBS1, RAD50, RAD51, and TOPBP1 in ovarian cancer risk. In BRCA1 carriers, no associations were observed with ATM, BARD1, CTIP, RAD50, RAD51, or TOPBP1. At BRIP1, an association was observed for one haplotype with a multiple testing corrected P (Pcorr) = 0.012, although no individual haplotype was significant. At MRE11, statistically significant associations were observed for one haplotype (Pcorr = 0.007). At NBS1, we observed a Pcorr = 0.024 for haplotypes. In BRCA2 carriers, no associations were observed with CTIP, NBS1, RAD50, or TOPBP1. Rare haplotypes at ATM (Pcorr = 0.044) and BARD1 (Pcorr = 0.012) were associated with ovarian cancer risk. At BRIP1, two common haplotypes were significantly associated with ovarian cancer risk (Pcorr = 0.011). At MRE11, we observed a significant haplotype association (Pcorr = 0.012), and at RAD51, one common haplotype was significantly associated with ovarian cancer risk (Pcorr = 0.026). Variants in genes that interact biologically withBRCA1 and/or BRCA2 may be associated with modified ovarian cancer risk in women who carry BRCA1/2 mutations. [Cancer Res 2009;69(14):5801–10]
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".