Review of the Etiology of Breast Cancer with Special Attention to Organochlorines as Potential Endocrine Disruptors
Bibliographic record
Abstract
Breast cancer is the most frequently diagnosed cancer among Canadian women, accounting for about 30% of all new cancer cases each year. Although the incidence of breast cancer has increased over the past 50 years, the cause of this rise is unknown. Risk factors for breast cancer may be classified into four broad categories: (1) genetic/familial, (2) reproductive/hormonal, (3) lifestyle, and (4) environmental. Established risk factors for breast cancer include older age, later age at first full-term pregnancy, no full-term pregnancies, postmenopausal obesity, and genetic factors. However, these known risk factors cannot account for the majority of cases. In the early 1990s, it was suggested that exposure to some environmental chemicals such as organochlorine compounds may play a causal role in the etiology of breast cancer through estrogen-related pathways. The relationship between organochlorines and breast cancer risk has been studied extensively in the past decade and more, and at this point there is no clear evidence to support a causal role of most organochlorine pesticides in the etiology of human breast cancer, but more evidence is needed to assess risk associated with polychlorinated biphenyls (PCBs). Future studies need to consider the combined effects of exposures, concentrate on vulnerable groups such as those with higher levels of exposure, only consider exposures occurring during the most etiologically relevant time periods, and more thoroughly consider gene-environment interactions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".