Bibliographic record
Abstract
Because of its increasing incidence, breast cancer is a significant burden for women worldwide. In industrialized countries, breast cancer is the second-leading cause of cancer-related deaths among women, and it is estimated that 1 in every 8 women will develop the disease during her lifetime. Sufficient evidence indicates that a number of genetic, environmental and lifestyle risk exposures during life may play important roles in the etiology of this disease. The purpose of this paper is to review some etiologic factors and underlying mechanisms in relation to breast cancer risk. Based on the published literature, there is sufficient evidence that some established factors are associated with breast cancer risk. Age, early age at menarche, late menopause, height, post-menopausal obesity, family history of breast cancer, ionizing radiation, oral contraceptives, hormonal replacement therapy, mammographic density, some gene mutations and clinical conditions, such as benign breast disease, are associated with an increased risk of breast cancer. The risk decreases with early childbearing, high parity and physical activity, and breastfeeding. Alcohol increases the risk, while caloric restriction may confer protection from breast cancer. Epidemiological evidence for other nutritional factors is insufficient. These results suggest that breast cancer is a multifactorial disease where genetic susceptibility, environment, nutrition and other lifestyle risk factors interact. Better identification of modifiable risk factors and risk reduction of breast cancer may allow implementation of useful strategies for prevention.
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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 | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".