OP07.01: Impact of pain and feelings of infertility on depression, anxiety and stress symptoms in individuals with endometriosis: an international survey
Bibliographic record
Abstract
Mental health symptoms and pain symptoms often coincide in chronic disease. People with endometriosis been shown to have higher incidences of mental health symptoms over people with chronic pain alone. The degree and nature of the relation however is not well established. This study aims to use established endometriosis symptom and mental health metrics to model the relationship between these two areas of patient experience. Our cross-sectional international online survey conducted between March 2021 and May 2021. The survey utilised validated questionnaires, including the Endometriosis Health Profile (EHP-30), the Depression Anxiety Stress Scales-21 (DASS-21), and the Posttraumatic Stress Disorder Checklist for DSM-5 (PCL-5). Pregnancy history was also recorded. Linear analysis was performed to assess relation between symptoms and mental health scores. 3,449 individuals with self-reported endometriosis participated in the survey. 118 were excluded. Most respondents (76.8%) were between the ages of 25 and 44, representing 81 different countries. There was a direct linear relation between pain severity, feelings of infertility, and depression, anxiety, stress and PTSD scores. There was an ‘interaction term’ between pain and infertility present for depression, stress and PTSD score. Mean DASS-21 scores were ‘normal’ however the mean PCL-5 score of 29.2 corresponds with moderate PTSD symptoms. Depression, anxiety and PCL-5 Scores were significantly less if prior pregnancy reported, P = 0.001, 0.006 and 0.006 respectively. Accessing fertility treatment reduced depression score (P = 0.014). Outcome of live birth versus no live birth did not alter this (P = 0.061). Severity of pain and feelings of infertility has a direct linear relation to DASS-21 and PCL-5 scores. Management of patients with self reported endometriosis may benefit from earlier evaluation and intervention from mental health specialists as part of the multi-disciplinary team.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".