New Insights Into the Pathogenesis of Ovarian Carcinoma
Bibliographic record
Abstract
Recent discoveries about the pathogenesis of ovarian cancer have suggested that it can no longer be thought of as a single entity, but that the histologically defined ovarian cancer subtypes are different diseases, with different precursor lesions and distinct biomarker expression profiles. Most serous carcinomas probably arise from the fallopian tube. Clear cell and endometrioid carcinomas are associated with endometriosis and likely originate from ectopic endometrium. The focus of large ovarian cancer screening trials has been detection of macroscopic ovarian abnormalities by ultrasonography and detection of serum biomarkers associated with the most common (serous) subtype of ovarian cancer. The only completed and phase three randomized controlled trial failed to achieve the objective of reducing ovarian cancer mortality and was not able to demonstrate a stage migration effect of the screening. Future screening strategies have to incorporate our growing understanding of each subtype of pelvic (ovarian or fallopian tube) cancer, its organ of origin, and disease-specific biomarkers. We review how our current understanding of pathogenesis should prompt a reexamination of data from ovarian cancer screening studies and discuss potential designs for future screening strategies.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".