Endometriosis, Anxiety, and Atherosclerosis: A Study of Eight Million Young Hospitalized Women in the USA
Bibliographic record
Abstract
OBJECTIVE: In recent years, several studies have proposed an association between endometriosis and various cardiovascular diseases. Our study evaluated the association between endometriosis and atherosclerosis in patients under 35 years of age using a large population database. DESIGN: This was a cross-sectional retrospective population-based study. PARTICIPANTS/MATERIALS, SETTING, METHODS: We used the data of more than eight million hospitalized women under 35 years of age who were registered in one of the hospitals participating in the Healthcare Cost and Utilization Project - National Inpatient Sample (HCUP NIS) during the study period of 2007-2014. The prevalence of endometriosis, atherosclerosis, and related conditions was estimated, and logistic regression model was used to examine the association. RESULTS: In the period of study of 8,061,754 patients, we noted an upward pattern for the prevalence of atherosclerosis and a downward trend for endometriosis. Adjusting the analysis for sociodemographic characteristics and comorbidities, the probability of being diagnosed with atherosclerosis was 42% higher in patients with endometriosis (odds ratio [OR] = 1.421; 95% confidence interval [CI]: 1.058-1.910); 35% higher in patients with anxiety (OR = 1.352; 95% CI: 1.249-1.464); and three times higher in women with both endometriosis and anxiety (OR = 3.075; 95% CI: 1.969-4.803) compared to women without those conditions. LIMITATIONS: In HCUP NIS databases, some information such as the severity of disease, laboratory findings, or medical treatment is not available. CONCLUSION: The strong association between endometriosis and atherosclerosis suggests that they may share a similar mechanism possibly endothelial dysfunction related to chronic inflammation. Further studies on the potential role of psychological conditions, such as anxiety, on systemic inflammatory diseases are also deemed timely and important.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".