Leading causes of death after a diagnosis of endometrial cancer: a systematic review and meta-analysis
Bibliographic record
Abstract
Objectives Despite curative treatment, an endometrial cancer diagnosis is associated with an elevated risk of death compared with age-matched women in the general population. This study aimed to quantify their risk of death from endometrial cancer, cardiovascular disease and other causes. Methods A systematic review of Medline, Embase and CENTRAL databases was performed to February 2024. Studies reporting cause of death following a diagnosis of endometrial cancer were included. Mortality rates and 95% confidence intervals were calculated using a random effects model. Heterogeneity was assessed through visual inspection of forest plots and the I 2 statistic. Risk of bias and evidence quality were appraised using the Newcastle-Ottawa Scale and GRADE, respectively. The effect of ethnicity, stage, grade and time from diagnosis was examined. Results In total, 22 studies including 323,551 participants were analysed and 102,711 (31.7%) died within 20 years of diagnosis, 62.6% (n=64,155) from non-endometrial cancer causes. In the twelve studies that reported cardiovascular death, 24.6% of participants (n=24,309) died from cardiovascular disease. Those with local disease at presentation were more likely to die from non-endometrial cancer causes than those with advanced disease at presentation (48.9% vs. 13.5%). Two studies reported cause of death by ethnicity; overall, Black individuals were more likely to die than individuals of White or Other ethnicities (40.8% vs. 27.9% vs. 18.9%). Deaths related to non-endometrial cancer causes, including cardiovascular disease, overtook endometrial cancer-specific deaths >5 years after diagnosis. Significant heterogeneity was noted, despite sub-group analyses, and the findings were based on very-low certainty evidence. Conclusion Individuals with a history of endometrial cancer are at increased risk of death from other causes. Oncology follow-up appointments provide the ideal opportunity to optimise cardiovascular risk factors to reduce preventable deaths. Future research needs to reflect the global majority.
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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 | 0.015 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.040 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".