Clinicopathological Correlation of Endometrial Thickness and Ovulatory Patterns in PCOS Women: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Background: Polycystic Ovary Syndrome (PCOS) is an endocrine condition that causes trouble with ovulation, increased male hormone levels, and ovaries filled with cysts. The aim of the systematic review and meta-analysis was to provide an evaluation of the effect of polycystic ovary syndrome (PCOS) on the endometrial thickness and histopathological abnormalities in women. Methods: The present systematic review and meta-analysis have been performed following PRISMA 2020 recommendations. Up to May 2025, four databases were searched to capture the English-language studies. Research articles with observational, cross-sectional, or retrospective cohort design that compared PCOS to non-PCOS sub-groups were included in the trial. The Newcastle-Ottawa Scale was used in determining the risk of bias. Meta-analysis was performed using RevMan 5.4.1 software, using a random-effects model. The degree of heterogeneity was determined as I2. Results: Five studies that included 20,914 participants were included. Meta-analysis indicated an insignificant difference between the Endometrial thickness (EMT) of population groups measured (SMD: 0.28; 95 % CI: -0.29 to 0.85; p > 0.05) and high heterogeneity (I2 = 88%). The pooled OR indicated a significantly elevated risk of endometrial hyperplasia or abnormal histology in women with menstrual irregularities (OR: 1.31; 95% CI: 1.09-1.57; p < 0.05). Subgroup and sensitivity confirmation effects were established. Discussion: Although endometrial thickness alone does not seem to vary significantly, menstrual irregularities in PCOS are related to a higher prevalence of endometrial hyperplasia. A key limitation of this review is the small number of studies, limiting the generalizability of the findings. Further studies are required to validate the usefulness of histological evaluation in PCOS patients.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.009 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".