Children and Adolescents with Co-Occurring Attention-Deficit/Hyperactivity Disorder and Autism Spectrum Disorder: A Systematic Review of Multimodal Interventions
Bibliographic record
Abstract
Background/Objectives: The co-occurrence of Attention-deficit/hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD) is very common and worsens adaptive functioning. This systematic review evaluates both pharmacological and non-pharmacological interventions in this underserved population. Methods: Registered on PROSPERO (CRD42024526157), a systematic search was conducted on PubMed, Embase, and Web of Science until 5 April 2025. The review includes (a) pilot studies and RCTs, (b) participants aged <18 years, (c) diagnoses of ASD and ADHD based on DSM-IV/V or ICD-9/10, (d) at least one group receiving any intervention, and (e) publications in English, Italian, Spanish, or German. Newcastle Ottawa Scale tools for non-randomized studies and the Cochrane Risk of Bias Tools for randomized controlled trials were used to assess studies’ quality. Results: A total of 32 studies were included: 87.5% concerning pharmacological treatments. Specifically, methylphenidate (MPH, n = 11), atomoxetine (ATX, n = 11), guanfacina (n = 4), clonidine (n = 1), or atypical antipsychotics (n = 1) were examined. MPH and ATX were most frequently studied, with both showing positive effects in reducing ADHD core symptoms compared to placebo. ATX also reduces stereotyped behaviors and social withdrawal, although more withdrawals due to adverse events (AEs) were reported for ATX than MPH. Four studies (12.5%) examined non-pharmacological interventions, including treatment with virtual reality tools, digital platforms, educational animations, and biomedical protocols; improvements in emotion recognition, behavioral regulation, attention, and social functioning were found. Conclusions: While limited data prevent definitive conclusions, MPH and ATX appear to be relatively safe and effective on hyperactivity-impulsivity symptoms, even in individuals with ASD. Evidence on non-pharmacological treatments is limited, and further studies are needed to better establish their therapeutic potential.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".