The association between endometriosis and risk of endometrial cancer and breast cancer: a meta-analysis
Bibliographic record
Abstract
Abstract Purpose Endometriosis (EMS) is confirmed pathophysiologically to be an estrogen-dependent disease, similar to endometrial hyperplasia/cancer and breast cancer. Epidemiological and biological data on endometriosis might explain links between endometriosis and these cancers. We sought to identify the differences in the risk of endometrial cancer and breast cancer between women with and women without endometriosis. Methods We searched PubMed, EMBASE, the Cochrane Library, and four Chinese databases (CNKI, VIP, WanFang, CBM) to identify relevant studies published online between January 2011 and March 2021. In our meta-analysis, we used the Newcastle–Ottawa Scale (NOS) to evaluate the design and quality of all studies, and we calculated the pooled risk ratio (RR) using the random model. The Q test and I2 were used to evaluate the degree of heterogeneity of eligible studies. We used funnel plots and Begg’s and Egger’s tests to assess publication bias. Results Of the 1369 articles, we finally included 14 cohort studies and seven case–control studies. Data from large cohort and case–control studies indicate that women with endometriosis had an increased risk of both endometrial cancer [RR, 1.662; 95% CI, (1.148–2.407)] and breast cancer [RR, 1.082; 95% CI, (1.001–1.169)]. Conclusion Endometriosis can increase the risk of endometrial cancer and breast cancer, and women with endometriosis are recommended to receive routine screening in long-term management.
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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 | 0.014 | 0.026 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.062 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 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".