Risk of endometrial cancer in patients with polycystic ovarian syndrome: A meta‑analysis
Bibliographic record
Abstract
While existing literature suggests an association between polycystic ovarian syndrome (PCOS) and endometrial cancer, the sparsity and inconsistency of current evidence indicates a lack of clarity regarding the exact strength of this association. It also remains uncertain whether the degree of risk of disease is affected by confounding factors, such as age and body mass index (BMI). The present meta‑analysis is aimed to quantify the risk of endometrial cancer in female subjects with PCOS compared to those without PCOS. PubMed, MEDLINE, EMBASE, Scopus and Cochrane were searched from inception to October 31, 2022, to identify peer‑reviewed case‑control, cohort and cross‑sectional studies that assessed the association between endometrial cancer and PCOS and contained original data. Two researchers independently extracted data and performed quality assessment using the Newcastle‑Ottawa criteria. Pooled odds ratios (ORs) were calculated using the random‑effect model and inverse variance. The degree of heterogeneity was assessed using I² statistics. A total of 10 relevant studies were identified and included in the meta‑analysis (comprising 12,248 female patients with PCOS and 54,120 controls). Females with PCOS had a significantly increased odds of developing endometrial cancer as compared to those without PCOS [OR, 4.07; 95% confidence interval (CI), 2.13‑7.78; P<0.0001]. When postmenopausal subjects (age, >54 years) were excluded from the meta‑analysis, the odds increased further (OR, 5.14; 95% CI, 3.22‑8.21; P<0.00001). Patients with PCOS are up to 5 times more likely to develop endometrial cancer compared to those without PCOS. Larger, prospective studies that are well‑controlled for confounding factors, such as BMI, are required.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.049 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".