Catastrophizing: a predictor of persistent pain among women with endometriosis at 1 year
Bibliographic record
Abstract
BACKGROUND: Endometriosis is the most common gynecological diagnosis among women with chronic pelvic pain, but the underlying mechanisms of endometriosis-associated chronic pelvic pain remain unclear. Therefore, the objective of this study was to determine the biopsychosocial predictors of pain improvement among women with endometriosis. METHODS: One hundred and fifteen women who presented for treatment of endometriosis-associated chronic pelvic pain at a tertiary referral center at a university-based hospital participated in this prospective observational study of clinical practice. Participants completed questionnaires assessing pain, mental health and catastrophizing at entry and 1 year follow-up. The main outcome measure assessed was the interval change in pain report using the McGill pain 1uestionnaire. RESULT(S): On average, participants experienced a 37.4% reduction in interval pain (P < 0.001). Adjusted for baseline pain, nulliparity (P = 0.002) and catastrophizing (P = 0.04) were associated with decreased probability of interval improvement in pain. Those referred for physical therapy had less interval pain improvement (P = 0.04). However, undergoing hysterectomy was a strong predictor of improvement in pain (P = 0.008). CONCLUSION(S): Our study suggests that chronic pain in endometriosis may be more akin to other idiopathic pain disorders. Specifically, biopsychosocial variables, such as catastrophizing, play an important role in reported severity. Further research on biopsychosocial correlates of chronic pelvic pain in endometriosis is warranted.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".