Rationale for Developing a Specimen Bank to Study the Pathogenesis of High-Grade Serous Carcinoma: A Review of the Evidence
Bibliographic record
Abstract
Women with clinically detected high-grade serous carcinomas (HGSC) generally present with advanced-stage disease, which portends a poor prognosis, despite extensive surgery and intensive chemotherapy. Historically, HGSCs were presumed to arise from the ovarian surface epithelium (OSE), but the inability to identify early-stage HGSCs and their putative precursors in the ovary dimmed prospects for advancing our knowledge of the pathogenesis of these tumors and translating these findings into effective prevention strategies. Over the last decade, increased BRCA1/2 mutation testing coupled with performance of risk-reducing surgeries has enabled studies that have provided strong evidence that many, but probably not all, HGSCs among BRCA1/2 mutation carriers appear to arise from the fallopian tubes, rather than from the ovaries. This shift in our understanding of the pathogenesis of HGSCs provides an important opportunity to achieve practice changing advances; however, the scarcity of clinically annotated tissues containing early lesions, particularly among women at average risk, poses challenges to progress. Accordingly, we review studies that have kindled our evolving understanding of the pathogenesis of HGSC and present the rationale for developing an epidemiologically annotated national specimen resource to support this research. Cancer Prev Res; 9(9); 713-20. ©2016 AACR.
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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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".