The association between childhood maltreatment and pain catastrophizing in individuals with immune-mediated inflammatory diseases
Bibliographic record
Abstract
OBJECTIVE: Childhood maltreatment is associated with pain catastrophizing. Both childhood maltreatment and pain catastrophizing are prevalent in certain immune-mediated inflammatory disease (IMID) populations. However, it is unknown whether childhood maltreatment contributes to the high rates of pain catastrophizing in IMID cohorts. We assessed the relationship between childhood maltreatment and pain catastrophizing in individuals with IMID, and whether this differed across IMID. METHODS: Between November 2014 and July 2016 we recruited individuals with multiple sclerosis (MS), inflammatory bowel disease (IBD), and rheumatoid arthritis (RA). Participants completed the Childhood Trauma Questionnaire-Short Form, the Pain Catastrophizing Scale, and Hospital Anxiety and Depression Scale. We tested the association between childhood maltreatment and pain catastrophizing using multivariable logistic regression. RESULTS: We included 577 individuals with IMID (MS: 232, IBD: 215, RA: 130). Overall, 265 (46%) participants with IMID reported any childhood maltreatment, with the most common type of maltreatment being emotional neglect. Childhood maltreatment was associated with pain catastrophizing (OR 3.32; 95% CI 1.89-5.85) independent of other risk factors, including sociodemographics and symptoms of anxiety and depression. CONCLUSION: Pain catastrophizing is highly prevalent in our IMID population, and strongly associated with childhood maltreatment in this population. Interventions that consider childhood maltreatment and pain catastrophizing should be incorporated into the clinical management of IMID patients.
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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 | 0.006 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".