Risk Factors for Ovarian Cancer: An Overview with Emphasis on Hormonal Factors
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it