Mammographic density and familial breast cancer: Comparison of related and unrelated controls
Bibliographic record
Abstract
B78 Background: Mammographic density (MD) is a strong and highly heritable risk factor for breast cancer with features of an intermediate phenotype for breast cancer. Some of the genes associated with MD may also be associated with risk of breast cancer, and MD may explain some of the effect of family history on risk. We would like to examine whether mammographic density is a risk modifier for women with a family history of breast cancer or who are carriers of BRCA1 or 2 mutations. However, family history and ethnicity are both very complex variables, and thereby more difficult to match. Sister controls may have an advantage, as you are able to match completely on the more difficult variables that have been shown to influence density. As density is largely genetically determined, this may result in overmatching on MD between the case and the related controls. This study seeks to examine the potential effects of using controls related to cases (e.g. sisters) or unrelated controls. >Methods: We formed triplets each consisting of a case diagnosed with invasive breast cancer, a full-blood unaffected sister, and an unrelated control. There were 236 triplets, for a total of 708 participants with mammograms. They were all matched within five years to age at mammogram. Randomly ordered images were measured “blindly” without any knowledge of the subjects. Total breast area and mammographic dense area were measured, and percent mammographic density, the outcome variable, calculated from these two measurements. >Results: We compared percent MD in cases and 2 groups of controls. One control group were unaffected sisters of the cases (n=236 case-control pairs), and other control group was comprised of 236 unrelated individuals. Statistically significant correlations in MD were seen between cases and their sister controls (r= 0.40 (95% CI: 0.28, 0.50; p Conclusions: The difference in mammographic density between cases and controls were similar for sister and for unrelated controls. The use of sisters as controls does not therefore introduce overmatching on MD, but does have the advantage of reducing the variance in the difference in MD between cases and controls.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".