Psychiatric Morbidity Is Common Among Children With Juvenile Idiopathic Arthritis: A National Matched Cohort Study
Bibliographic record
Abstract
OBJECTIVE: Juvenile idiopathic arthritis (JIA) is a chronic rheumatic disease that causes joint inflammation and pain. Previous studies have indicated affected mental health and increased risk of psychiatric conditions among patients with JIA. We aimed to explore differences in psychiatric morbidity between children with JIA and their peers. We further studied if parental socioeconomic status (SES) influences the association between JIA and the risk of psychiatric morbidity. METHODS: We used a matched cohort design to estimate the association between JIA and psychiatric disease. Children with JIA, born between 1995 and 2014, were identified in Danish national registers. Based on birth registers, we randomly selected 100 age- and sex-matched children per index child. Index date was the date of the fifth JIA diagnosis code or the date of matching for reference children. End of follow-up was the date of psychiatric diagnosis, death, emigration, or December 31, 2018, whatever came first. Data were analyzed using a Cox proportional hazard model. RESULTS: We identified 2086 children with JIA with a mean age at diagnosis of 8.1 years. Children with JIA had a 17% higher instantaneous risk of a psychiatric diagnosis when compared with the reference group, with an adjusted hazard ratio of 1.17 (95% CI 1.02-1.34). Relevant associations were found only for depression and adjustment disorders. Stratifying our analysis for SES showed no modifying effect of SES. CONCLUSION: Children with JIA had a higher risk of psychiatric diagnoses compared to their peers, especially diagnoses of depression and adjustment disorders. The association between JIA and psychiatric disease did not depend on parental SES.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".