Endometriosis, Raynaud’s Syndrome, and Migraine: A Retrospective Study of 12 Million Women
Bibliographic record
Abstract
Objective: Our study focused on evaluating a possible relationship between endometriosis, Raynaud's syndrome, or migraine among women. DESIGN: This was a cross-sectional population-based study. Participants/Materials; Setting; Methods: We used 12,684,067 hospitalized patient records in the Healthcare Cost and Utilization Project (HCUP) database between 2007 and 2014. We calculated the prevalence of endometriosis, Raynaud's syndrome, and migraine. We also evaluated the possible role of depression, anxiety, and autoimmune diseases to eliminate confounding factors. Unadjusted and adjusted multivariate logistic regressions were applied to evaluate the relationship between variables. RESULTS: Unadjusted analysis revealed a strong association between endometriosis and Raynaud's syndrome and migraine (OR = 2.491; 95% CI: 1.677-3.699). After adjusting for sociodemographic characteristics as well as depression and anxiety, the association remained significant (OR = 1.779; 95% CI: 1.166-2.716). Among younger patients aged 18-35 with endometriosis, the associations were stronger with Raynaud's syndrome (adjusted OR = 1.61, 95% CI = 1.20-2.16) and migraine (adjusted OR = 2.59, 95% CI = 2.47-2.72). LIMITATIONS: The HCUP database is cross-sectional in nature, and hence, we could not establish the temporal relationship between endometriosis, Raynaud's syndrome, and migraine. Also, the severity of endometriosis and the treatment received by the patients were not included in the dataset, and it prevented us from investigating the role of potential confounding factors. CONCLUSION: Our study suggests an association between endometriosis, Raynaud's syndrome, and migraine. It is possible that these conditions share a similar mechanism possibly vascular reaction and endothelial dysfunction related to chronic inflammation. .
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".