Childhood Maltreatment and Psychiatric Comorbidity in Immune-Mediated Inflammatory Disorders
Bibliographic record
Abstract
OBJECTIVE: To determine whether childhood maltreatment is associated with immune-mediated inflammatory disorders (IMIDs; multiple sclerosis [MS], inflammatory bowel disease [IBD], and rheumatoid arthritis [RA]). We further aimed to determine the relationship between maltreatment and psychiatric comorbidity in IMIDs and whether these relationships differed across IMID. METHODS: Six hundred eighty-one participants (MS, 232; IBD, 216; RA, 130; healthy controls, 103) completed a structured psychiatric interview to identify psychiatric disorders, and the Childhood Trauma Questionnaire to evaluate five types of maltreatment: emotional abuse, physical abuse, sexual abuse, emotional neglect, and physical neglect. We evaluated associations between maltreatment, IMID, and psychiatric comorbidity using multivariable logistic regression models. RESULTS: The prevalence of having ≥1 maltreatment was similar across IMID but higher than in controls (MS, 63.8%; IBD, 61.6%; RA, 62.3%; healthy controls, 45.6%). Emotional abuse was associated with having an IMID (adjusted odds ratio [aOR] = 2.37; 1.15-4.89). In the sex-specific analysis, this association was only present in women. History of childhood maltreatment was associated with a lifetime diagnosis of a psychiatric disorder in the IMID cohort (OR = 2.24; 1.58-3.16), but this association did not differ across diseases. In those with IMID, total types of maltreatments (aOR = 1.36; 1.17-1.59) and emotional abuse (aOR = 2.64; 1.66-4.21) were associated with psychiatric comorbidity. CONCLUSIONS: Childhood maltreatment is more common in IMID than in a healthy population and is associated with psychiatric comorbidity. Given the high burden of psychiatric disorders in the IMID population, clinicians should be aware of the contribution of maltreatment and the potential need for trauma-informed care strategies.
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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 | 0.001 |
| 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) | 0.002 | 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".