Risk Factors for Ovarian Cancer: An Overview with Emphasis on Hormonal Factors
Bibliographic record
Abstract
Ovarian cancer is the fifth most frequently occurring cancer among women and leading cause of gynecological cancer deaths in North America. Although the etiology of ovarian cancer is not clear, certain factors are implicated in the etiology of this disease, such as ovulation, gonadotropic and steroid hormones, germ cell depletion, oncogenes and tumor suppressor genes, growth factors, cytokines, and environmental agents. Family history of breast or ovarian cancer is a prominent risk factor for ovarian cancer, with 5-10% of ovarian cancers due to heritable risk. Reproductive factors such as age at menopause and infertility contribute to greater risk of ovarian cancer, whereas pregnancy, tubal ligation, and hysterectomy reduce risk. Oral contraceptive (OC) use has clearly been shown to be protective against ovarian cancer. In contrast, large epidemiologic studies found hormone replacement therapy (HRT) to be a greater risk factor for ovarian cancer. The marked influence of hormones and reproductive factors on ovarian cancer suggests that endocrine disrupters may impact risk; however, there is a notable lack of research in this area. Lifestyle factors such as cigarette smoking, obesity, and diet may affect ovarian cancer risk. Exposure to certain environmental agents such as talc, pesticides, and herbicides may increase risk of ovarian cancer; however, these studies are limited. Further research is needed to strengthen the database of information from which an assessment of environmental and toxicological risk factors for ovarian cancer can be made.
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".