Bibliographic record
Abstract
The radiographic appearance of the breast on mammography varies among women, and reflects variations in breast tissue composition and the different X-ray attenuation characteristics of these tissues. Fat is radiologically lucent and appears dark on a mammogram. Connective and epithelial tissues are radiologically dense and appear light. These variations in appearance are commonly described as the percentage of the breast image that is radiologically dense, or as percent mammographic density (PMD). There is now extensive evidence that PMD is a risk factor for breast cancer, with a 4- to 6-fold gradient in risk between women with 75% or more PMD compared with those with 10% or less. However, the accuracy of risk prediction in individual women is modest. The extent of PMD is associated inversely with greater age, parity, and weight, and is reduced by the menopause and by tamoxifen. PMD is positively associated with greater height, a family history of breast cancer, and is increased by combined hormone therapy. The relative risk associated with density is substantially larger than the relative risk of breast cancer associated with a family history of the disease or any of the menstrual and reproductive risk factors. It is estimated that the risks of breast cancer attributable to density of 50% or more may be 16% for all breast cancers. Although combined hormone therapy and tamoxifen respectively increase a decrease both PMD and breast cancer risk, there is as yet insufficient evidence to use PMD as a surrogate marker for breast cancer.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".