Trends in the Incidence of Ovarian Cancer Among Premenopausal and Postmenopausal Women in the United States, 2001 to 2021
Bibliographic record
Abstract
BACKGROUND: Ovarian cancer remains the deadliest and leading cause of gynecological cancer-associated mortality in the US. The aim of this study was to characterize the trends in the incidence of ovarian cancer between premenopausal and postmenopausal women to inform future targeted interventions. METHODS: This population-based cross-sectional study analyzed data from the US Cancer Statistics (USCS) database, which covered the whole of the US population between 2001 and 2021. Joinpoint regression was used to compute the average annual percentage change (APC) with 95% confidence interval (CI) and age-standardized incidence rates per 1,000,000 population. RESULTS: The results showed that the IR of ovarian cancer declined between 2001 and 2021. Postmenopausal women had greater decreases in the IR of ovarian cancer compared to premenopausal women who showed a small decline. When stratified by race/ethnicity, non-Hispanic American Indian/Alaska Native women aged 20-49 years experienced an increase in the IR of ovarian cancer (APC = 2.4; 95% CI 0.9 to 4.1) compared to other racial/ethnic groups which showed a decline. Joinpoint trend analyses identified one inflection point in localized ovarian cancer incidence trends among all three age groups: an initial decline from 2001 to 2011 among women 20-49 years old and 65+ years old, and from 2001 to 2012 among women 50-64 years old, followed by an upward trend thereafter to 2021. Similarly, there was one inflection point in the IR of ovarian cancer for the clear cell and endometrioid types among women aged 20-49 years old. CONCLUSIONS: The IR of ovarian cancer in the US declined significantly among postmenopausal compared to premenopausal women, for whom the IR of ovarian cancer decreased only slightly. Although encouraging, these findings show a need for continued efforts to improve early detection and prevention strategies to mitigate the burden of this deadly disease.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".