European trends in ovarian cancer mortality, 1990–2020 and predictions to 2025
Bibliographic record
Abstract
INTRODUCTION: Over the last decades, ovarian cancer mortality in Europe has been decreasing, but disparities in trends were observed. In this paper, we analysed ovarian cancer mortality trends in Europe over the period 1990-2020 and predicted the number of deaths and rates by 2025. METHODS: We extracted population and death certification data from ovarian cancer in women for 31 European countries, between 1990 and 2020 from the World Health Organization database. We computed age-standardised mortality rates (ASMR) per 100,000 women-years, based on the world standard population. We also obtained predictions for 2025 using a joinpoint regression model and calculated the number of avoided deaths over the period 1994-2025. RESULTS: Over the observed period, mortality from ovarian cancer showed a favourable pattern in most countries. In the EU-27, rates declined by 5.9% from 2010-2014 to 2015-2019, reaching an ASMR of 4.66/100,000. During the same period, the decline in ovarian cancer mortality was more pronounced in the EU-14 countries (-7.0%) compared to Transitional countries (-2.1%). Declines were also observed in the United Kingdom, to reach an ASMR of 5.29. Decreases in mortality from ovarian cancer are predicted until 2025, to 4.17/100,000 for the EU-27. CONCLUSIONS: Favourable trends in ovarian cancer mortality are expected to persist in Europe and can be mainly attributed to the increased use of oral contraceptives in subsequent generations of European women. Decreased use of menopausal Hormone Replacement Therapy and improved diagnosis and management may also have played a role.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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 teacher head, 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".