Prevalence of co-occurring conditions in children and adults with autism spectrum disorder: A systematic review and meta-analysis
Bibliographic record
Abstract
This systematic review estimates the prevalence of co-occurring conditions (CCs) in children and adults with autism. A comprehensive search strategy consulting existing guidelines, diagnostic manuals, experts, carers, and autistic people was developed. PubMed and PsycInfo databases from inception to May 2022 were searched. PROSPERO registration: CRD42019132347. Two blind authors screened and extracted the data. Prevalence estimates for different CCs were summarized by using random effects models. Subgroup analyses were performed for age groups (children/adolescents vs adults) and study designs (population/registry-based vs clinical sample-based). Of 19,932 studies, 340 publications with about 590,000 participants were included and meta-analyzed to estimate the prevalence of 38-point prevalence, 27-lifetime, and 3 without distinction between point and lifetime prevalence. Point prevalence of developmental coordination disorder, sleep-wake problem, gastrointestinal problem, ADHD, anxiety disorder, overweight/obesity, feeding and eating disorder, elimination disorder, disruptive behavior, and somatic symptoms and related disorder were the most frequent CCs. Prevalence differed depending on the age group and study design. Knowing specific CCs linked to autism helps professional investigations and interventions for improved outcomes.
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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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.023 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".