The Importance of Screening for Early Detection of Ovarian Cancer: Epidemiological Review
Bibliographic record
Abstract
Abstract – Objective: Ovarian cancer, although not possessing a high incidence, is still the most common cancer-related deaths among women diagnosed with a gynecologic malignancy. The present study aims to highlight the epidemiology, risk factors of this disease and the significance of development of improved early detection strategies. Materials and Methods: This study was conducted using current published English studies by searching PubMed and Google Scholar. The search strategy included the keywords “ovarian cancer”, “diagnosis”, “risk factors”, “screening”, “epidemiology”. Studies on incidence and mortality were also considered. Case reports were excluded.Results: The highest incidence and mortality rates are observed in Central and Eastern Europe, while rates are relatively low in Asia and Africa. These rates are highest among the white population (14.3 per 100,000) and lowest among blacks (10 per 100,000) and Asians (9.7 per 100,000). The risk factors for this disease includes a family history, hormonal factors, nutrition and diet and physical activity, with some of them playing protective roles in reducing risk of ovarian cancers. There are no reliable screening methods for ovarian cancers. The most common diagnosis methods include a transvaginal ultrasound and a blood test to detect CA125 markers.Conclusions: The mortality rate of ovarian cancer is gradually increasing; thus, preventative measures are required to reduce lifetime risk of ovarian cancers and improve mortality rate.
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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".