Ovarian Cancer: From Precursor Lesion Identification to Population-Based Prevention Programs
Bibliographic record
Abstract
variants elevate risk, most HGSC cases occur in individuals without known genetic variants, necessitating prevention strategies for people without known high-risk genetic variants. Effective prevention programs are also needed due to the lack of traditional screening options. An emerging primary prevention strategy is opportunistic salpingectomy, which involves removing fallopian tubes during another planned pelvic surgery. Opportunistic salpingectomy offers a safe and cost-effective preventative option that is gaining global adoption. With the publication of the first cohort study of patients who underwent salpingectomy, specifically for cancer prevention, attention has turned to broadening opportunities for salpingectomy in addition to more targeted approaches. Prevention opportunities are promising with increasing adoption of salpingectomy and the increased understanding of the etiology of the distinct histotypes of ovarian cancer. Yet, further research on targeted risk-reducing salpingectomy with thoughtful consideration of equity is necessary to reduce death and suffering from ovarian cancer.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".