Pain Catastrophizing and Pain Health-Related Quality-of-Life in Endometriosis
Bibliographic record
Abstract
OBJECTIVES: To determine if pain catastrophizing is independently associated with pain health-related quality-of-life (HRQoL) in women with endometriosis, independent of potential confounders. MATERIALS AND METHODS: Analysis of cross-sectional baseline data from a prospective database at a tertiary referral center for endometriosis/pelvic pain. Referrals to the center were recruited between December 2013 to April 2015, with data collected from online patient questionnaires, physical examination, and review of medical records. The primary outcome was HRQoL as measured by the 11-item pain subscale of the Endometriosis Health Profile-30 questionnaire. The Pain Catastrophizing Scale was the independent variable of interest. Other independent variables (potential confounders) included other psychological measures, pain severity, comorbid pain conditions, and social-behavioral and demographic variables. Multivariable linear regression was used to control for these potential confounders and assess independent associations with the primary outcome. RESULTS: In total, 236 women were included (87% consent rate). The mean age was 35.0±7.3 years, and 98 (42%) had stage I to II endometriosis, 110 (47%) had stage III to IV endometriosis, and 28 (11%) were of unknown stage after review of operative records. Regression analysis demonstrated that higher pain catastrophizing (P<0.001), more severe chronic pelvic pain (P<0.001), more severe dysmenorrhea (P<0.001), and abdominal wall pain (positive Carnett test) (P=0.033) were independently associated with worse pain HRQoL. DISCUSSION: Higher pain catastrophizing was associated with a reduced pain HRQoL in women with endometriosis at a tertiary referral center, independent of pain severity and other potential confounders.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".