Association Between Pelvic Inflammatory Disease and Risk of Endometriosis: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Endometriosis is a common chronic disorder, which leads to dysmenorrhea, dyspareunia, pelvic chronic pain, and infertility. It affects ∼6% to 10% of the general female population. However, the etiology of endometriosis remained unclear. We aimed to systematically assess the association between pelvic inflammatory disease (PID) and the risk of endometriosis. Materials and Methods: Eligible studies published until May 21, 2022, were retrieved from the PubMed, EMBASE, and Web of Science databases. The studies were included based on the following criteria: (1) original articles on the association between PID and risk of endometriosis; (2) randomized controlled trials and cross-sectional, case–control, and cohort studies; and (3) studies involving humans. The Newcastle-Ottawa Quality Assessment Scale was used to assess the quality of the studies included in this systematic review. The association between PID and risk of endometriosis was evaluated using the overall odds ratio (OR) and correlative 95% confidence interval (CI). Results: The meta-analysis included 14 studies with 747,733 patients. The mean prevalence of PID in women with endometriosis was 33.80%. Our quantitative synthesis revealed that endometritis was associated with a significantly increased risk of endometriosis (OR: 1.63, 95% CI: 1.53–1.74, I 2 = 59%). Conclusion: We study a statistically significant association between PID and the risk of endometriosis. In particular, endometritis might play an important role in endometriosis, based on the lower heterogeneity of the subgroup analysis. This finding suggests that reducing the incidence of endometritis might aid in the prevention and treatment of endometriosis.
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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 | 0.012 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.032 |
| Bibliometrics | 0.008 | 0.009 |
| 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".