Abstract P3-08-04: Aspirin and breast cancer risk for <i>BRCA1</i> and <i>BRCA2</i> mutation carriers
Bibliographic record
Abstract
Abstract Background : Although epidemiologic studies have found evidence that aspirin use may be inversely associated with breast cancer (BC) risk, little is known about whether this applies to BRCA1 and BRCA2 mutation carriers. Methods : We compared aspirin use in 613 women with BRCA1 or BRCA2 mutations from the six centers of the Breast Cancer Family Registry (BCFR) who were recruited at baseline and completed a questionnaire at 10 year follow-up. We defined cases as carriers with BC (n = 215 with BRCA1 mutations and 137 with BRCA2 mutations) and controls as carriers unaffected with BC (n = 141 with BRCA1 mutations and 120 with BRCA2 mutations). We used logistic regression to estimate odds ratios and 95% confidence intervals separately by gene mutation type. Results : Three cases (1.4%) and 27 controls (19.1%) among the BRCA1 carriers and 3 cases (2.2%) and 25 controls (20.8%) among the BRCA2 carriers reported ever use of aspirin-based medications before diagnosis. Aspirin use before diagnosis was inversely associated with BC risk for both BRCA1 (OR, 0.13; 95% CI, 0.04-0.46 for ever vs. never use) and BRCA2 (OR, 0.12; 95% CI, 0.03-0.41 for ever vs. never use) carriers, after adjusting for age and center for BRCA1 carriers and age for BRCA2 carriers. Conclusion : If replicated by larger, prospective studies, aspirin use could become an inexpensive and acceptable risk-reducing measure for BRCA1 and BRCA2 mutation carriers. Citation Format: Naomi Kornhauser, Mary Beth Terry, Linda T Vahdat, Irene Andrulis, Saundra Buys, Mary Daly, Esther John, John L Hopper, Tessa Cigler. Aspirin and breast cancer risk for BRCA1 and BRCA2 mutation carriers [abstract]. In: Proceedings of the Thirty-Seventh Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2014 Dec 9-13; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2015;75(9 Suppl):Abstract nr P3-08-04.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.004 | 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".