Does mammographic density reflect the expression of breast cancer markers?
Bibliographic record
Abstract
Mammographic density reflects variation in breast tissue composition as detected on mammogram. It is associated with a number of well-known breast cancer risk factors and itself is considered one of the strongest risk factors for breast cancer. If the expression of several proteins and genes within the breast tissue influences mammographic density in the same way as it influences breast cancer risk, then mammographic density might serve as an intermediate biomarker in future epidemiological studies on breast cancer. This has the potential to provide a quick means for predicting the effect of changes in the breast microenvironment on breast cancer risk without having to wait for an eventual development of breast cancer. In this review, the expression of several proteins and genes (growth factors, enzymes, proteoglycans and pro-inflammatory markers) within the breast tissue is shown to be associated with mammographic density. These proteins and genes are suspected to play a role in breast carcinogenesis. More studies assessing differential expression of proteins and genes in mammary epithelium and stroma and their association with mammographic density among premenopausal and postmenopausal women are required. Identification of proteins and genes influencing mammographic density may provide further insight on the molecular causes of breast cancer.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".