Mammographic breast density and cancer risk: The radiological view
Bibliographic record
Abstract
Breast density refers to the presence of fibroglandular tissue in the breast. Several investigators have demonstrated that extensive mammographic density is strongly associated with increased risk of breast cancer. Density can be assessed either subjectively or quantitatively. Subjective measurements include Wolfe patterns, the Breast Imaging Reporting and Data System and the 6-category scale. However, quantitative density measurements, such as interactive thresholding, planimetry and volumetric density imaging, provide stronger estimates of risk than those which are simply qualitative. Breast density is of particular interest in assessing the etiology of breast cancer, but it may also have potential as a surrogate marker of risk in interventions designed to reduce the risk of breast cancer. However, a surrogate marker is only acceptable if it shows biological feasibility and statistical correlation. To date, we can say that there is undoubtedly an association between breast density and breast cancer, but it will not be until an intervention study using risk as an endpoint has firmly established the connection between change in density and change in risk that density will be absolutely acceptable as a surrogate for breast cancer risk.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.002 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".