Invasive Epithelial Ovarian Cancer Survival by Histotype and Disease Stage
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
Background: The understanding of ovarian cancer pathogenesis has recently shifted to recognize distinct changes in how ovarian cancer histotypes are defined. Using the 2014 World Health Organization (WHO) diagnostic guidelines, we classified ovarian cancer histotypes in Surveillance, Epidemiology, and End Results (SEER) cancer registry data and examined survival patterns by histotype and disease stage. Methods: We extracted data on 28 118 incident epithelial ovarian cancer cases diagnosed in 2004-2014 from SEER and defined histotype using the 2014 WHO guidelines (high-grade serous, low-grade serous, endometrioid, clear cell, mucinous, carcinosarcoma, and malignant Brenner tumors). By histotype and disease stage, we estimated Kaplan-Meier survival curves and calculated age-adjusted overall and cause-specific survival estimates. Cox proportional hazards regression models were used to estimate histotype-specific hazard ratios (HRs) and 95% confidence intervals (CIs) by disease stage while adjusting for age at diagnosis, region, race/ethnicity, and receipt of surgery. Results: Within two years after diagnosis, localized/regional-stage carcinosarcoma and distant-stage mucinous, clear cell, and carcinosarcoma had a higher risk of mortality compared with high-grade serous, with the most pronounced association for localized/regional carcinosarcoma (>1-2-year time period: HR = 3.81, 95% CI = 2.74 to 5.30) and distant-stage mucinous (0-1-year time period: HR = 3.87, 95% CI = 3.45 to 4.34). In the time period more than four to 10 years after diagnosis, hazard ratios for all histotypes relative to high-grade serous, irrespective of disease stage, were less than 1.00. Cumulatively, both localized/regional and distant-stage low-grade serous and endometrioid carcinomas had the most favorable outcomes. Conclusions: Our large study, which is representative of the United States population and incorporates the most current knowledge of ovarian cancer pathogenesis, highlights the need to recognize ovarian cancer as a set of distinct diseases and not a single entity. Only then will we be able to effectively target the unique features of each histotype to reduce ovarian cancer mortality.
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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) | 0.000 | 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 | 0.000 |
| 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