The Association Between Endometriosis Treatments and Depression and/or Anxiety in a Population-Based Pathologically Confirmed Cohort of People with Endometriosis
Bibliographic record
Abstract
Objective: Endometriosis patients have a high rate of co-occurring anxiety and depression. There is currently no literature investigating how this may affect endometriosis treatment and outcomes. This study examines the prevalence of depression and anxiety in a pathologically confirmed population-based endometriosis cohort and examines how endometriosis treatments and outcomes differ by the presence of co-occurring depression and/or anxiety. Methods: This retrospective cohort study using population-based administrative data sets included pathologically confirmed endometriosis patients identified from the complete pathology records of Vancouver Coastal Health Authority (British Columbia, Canada) between 2000 and 2008. These data were linked with population-based health data for follow-up to 2017. Bivariate analyses assessed differences between patients with depression and/or anxiety and those without. Odds ratios (ORs) were calculated to assess the odds of binary postsurgical outcomes. Results: Our final cohort consisted of 3815 patients. There were 603 patients (15.8%) with depression and/or anxiety. They were more likely to visit a physician for pelvic pain, more likely to take some hormonal medications, and more likely to fill prescription-level analgesics, including opioids both before and after surgery. They also had a significantly higher risk of reoperation for their endometriosis than people without co-occurring depression and/or anxiety (OR 1.32, 95% confidence interval [CI]: 1.07-1.61). Conclusion: Endometriosis patients with co-occurring depression and/or anxiety used more health services for pain, including prescription-level analgesics, and were more likely to have an endometriosis reoperation. We recommend that future study should aim to better understand the direction of this association between depression and/or anxiety and increased health services use.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".