Diagnosis, pelvic pain, and medication mediate cognitive empathic abilities among women with endometriosis or polycystic ovary syndrome
Bibliographic record
Abstract
Objective: Investigate how women with self-reported endometriosis, polycystic ovary syndrome (PCOS), or no known reproductive disease perform on the Reading the Mind in the Eyes task, a metric of social cognitive empathic abilities, and how these scores are mediated by self-reported pelvic pain, depression levels, and medication use. Design: Cross-sectional. Setting: Online survey. Population: Saskatchewan; mixed ethnicities. Methods: Participants ( n = 342) completed surveys on diagnoses of endometriosis and PCOS, levels of pelvic pain, medications, depression, and cognitive empathy (the Reading the Mind in the Eyes task, RMET). Results: Women with endometriosis showed lower scores on the RMET, compared to unaffected women and women with PCOS. However, among women with endometriosis, higher levels of pelvic pain and higher scores on an index of depression were associated with enhanced RMET performance. Among women with PCOS, medication with spironolactone, an androgen receptor antagonist, was associated with higher RMET scores, compared to women with PCOS who were not medicated or who were taking different medications. Conclusions: Symptoms (including pelvic pain), hormonal correlates, and some treatments of PCOS and endometriosis mediated variation in cognitive empathy.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".