Bibliographic record
Abstract
Considerable knowledge has been acquired about the determinants of female breast cancer. A positive family history among first-degree relatives is associated with an increased risk of breast cancer, as is a past history of benign proliferative breast disease. Risk increases with decreasing age at menarche, and with increasing age at first full term pregnancy and at menopause. Pregnancy is followed by a short-term increase in risk, followed by a long-term decrease. Risk increases with age. Evidence that environmental determinants are involved comes from the study of migrant populations: migrants assume the risk of breast cancer prevalent in their new environment, arguing against the hypothesis that risk is determined solely by genetic mechanisms. The best understood environmental determinant is ionizing radiation: it increases risk in a dose-dependent fashion. While ecologic studies of diet supported the hypothesis that high fat intake increases risk, cohort and case-control studies have yielded conflicting evidence, casting doubt on this hypothesis. However, weight gain during adult life is associated with an increased risk. Some drugs increase risk, specifically exogenous oestrogens and ethanol. The anti-oestrogen tamoxifen reduces risk. Recent evidence shows that both passive and active exposure to tobacco smoke is associated with an increased risk of breast cancer. Some women may be more susceptible to the effects of tobacco smoke on a genetic basis. Undoubtedly, other environmental determinants of breast cancer will be discovered.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".