Psychiatric comorbidity in childhood onset immune‐mediated diseases—A systematic review and meta‐analysis
Bibliographic record
Abstract
AIM: To estimate psychiatric comorbidity in childhood onset immune-mediated inflammatory diseases (IMID). METHODS: The PRISMA guidelines were followed, and the protocol was registered at Prospero (ID: CRD42021233890). Literature was searched in PubMed, PsycINFO and Embase. Original papers on prevalence rates of diagnosed psychiatric disorders and/or suicide in paediatric onset inflammatory bowel disease (pIBD), rheumatic diseases (RD) and autoimmune liver diseases were selected. Pooled prevalence rates of psychiatric disorders (grouped according to ICD-10 criteria) within the various IMID were calculated using random-effects meta-analysis. Risk of bias was evaluated by the Newcastle-Ottawa scale. RESULTS: Twenty-three studies were included; 13 describing psychiatric disorders in pIBD and 10 in RD. Anxiety and mood disorders were mostly investigated with pooled prevalence rates in pIBD of 6% (95% confidence interval (CI): 4%-9%) and 4% (95%CI: 2%-8%), respectively, in register-based studies, and 33% (95%CI: 25%-41%) and 18% (95%CI: 12%-26%), respectively, in studies using psychiatric assessment. In RD, rates were 13% (95%CI: 12%-15%) for anxiety disorders and 20% (95%CI: 15%-26%) for mood disorders based on psychiatric assessment. CONCLUSION: Anxiety and depression are commonly reported in childhood onset IMID. Physicians should be attentive to mental health problems in these patients as they seem overlooked.
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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 | 0.015 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.040 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".