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Record W4205899633 · doi:10.1111/apa.16246

Psychiatric comorbidity in childhood onset immune‐mediated diseases—A systematic review and meta‐analysis

2022· review· en· W4205899633 on OpenAlexaboutno aff
Sabine Jansson, Mikkel Malham, Vibeke Wewer, Charlotte Ulrikka Rask

Bibliographic record

VenueActa Paediatrica · 2022
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammatory Bowel Disease
Canadian institutionsnot available
FundersLundbeckfonden
KeywordsMedicineComorbidityPsychiatric comorbidityMeta-analysisPsychiatryMEDLINEImmune systemImmunologyInternal medicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.928
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.003
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.018
GPT teacher head0.281
Teacher spread0.263 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designMeta-analysis
Domainnot available
GenreReview

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".

Quick stats

Citations16
Published2022
Admission routes1
Has abstractyes

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