Bibliographic record
Abstract
Epidemiological evidence implicating anthropometric risk factors in breast cancer aetiology is accumulating. For premenopausal women, breast cancer risk increases with increasing height, but decreases with higher weight or body mass index, and no association with increased central adiposity exists. For postmenopausal women, an increased risk of breast cancer is found with increasing levels of all the anthropometric variables including height, weight, body mass index, waist-hip ratio, waist circumference and weight gain. Weight loss appears to decrease risk, particularly if it occurs later in life. Breast size may be a risk factor for breast cancer, however, the current evidence is inconclusive. Several hypothesized biologic mechanisms exist to explain how anthropometric factors influence breast cancer risk. Obesity may increase levels of circulating endogenous sex hormones, insulin and insulin-like growth factors that all, in turn, increase breast cancer risk. Genetic predisposition to obesity and to specific body fat distributions are also implicated. With obesity, there are increased levels of fat tissue that can store toxins and can serve as a continuous source of carcinogens. Recommendations for future research on anthropometric factors and breast cancer are provided. Sufficient evidence exists to support strategies to avoid weight gain throughout life as a means of reducing postmenopausal 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".