ReviewCureus2026
Effectiveness and Types of Interventions for Autism Spectrum Disorder: A Systematic Review, Meta-Analysis, and Meta-Regression.
Review in Cureus, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
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Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This systematic review and meta-analysis aimed to estimate the overall effectiveness of autism spectrum disorder (ASD) interventions and identify sources of heterogeneity using frequentist and Bayesian approaches. A systematic search of PubMed/MEDLINE, Embase, Web of Science, and Scopus was conducted for studies published between January 1, 2004, and April 30, 2025. Primarily, randomized controlled trials with extractable intervention outcomes were included. A total of 41 studies (n = 3,008) were synthesized using random-effects models (restricted maximum-likelihood (REML)), Bayesian hierarchical modeling, meta-regression, and sensitivity analyses following PRISMA guidelines. The pooled random-effects estimate showed a significant positive effect of ASD interventions (effect size = 0.506, 95% CI: 0.392-0.619; z = 8.72, p < 0.001), corresponding to an estimated success proportion of 62% (95% CI: 59%-65%). Heterogeneity was substantial (Qₑ (40) = 238.78, p < 0.001; I² = 82.45%; τ² = 0.069, 95% CI: 0.028-0.137; τ = 0.262), with H² = 5.70 and a wide prediction interval (-0.020 to 1.031), indicating strong between-study variability. Bayesian meta-analysis confirmed a comparable effect (posterior mean = 0.619 (62%), 95% CrI: 0.592-0.646), with τ = 0.273 and I² ≈ 82.5%; Markov Chain Monte Carlo (MCMC) diagnostics indicated stable convergence (R-hat ≈ 1.00). Publication bias analyses indicated significant funnel plot asymmetry (Egger-type regression: z = 3.429, p < 0.001; weighted regression: t = 9.573, p < 0.001), while rank correlation was non-significant (τ = -0.178, p = 0.103). Trim-and-fill analysis imputed 10 studies, reducing the pooled effect to 0.374 (37%; 95% CI: 0.258-0.491; τ = 0.338), although the effect remained significant (p < 0.001). Sensitivity analyses excluding influential studies yielded a stable effect (0.505 (51%), 95% CI: 0.401-0.609), with persistent heterogeneity (I² = 75.49%; Qₑ (38) = 190.21, p < 0.001; τ² = 0.043). Subgroup analyses showed highest effects for digital/technology-based interventions (0.672 (67%); I² = 0%), followed by nutritional (0.635 (64%); I² = 73.81%), behavioral (0.630 (63%); I² = 74.78%), and pharmacological (0.627 (63%); I² = 0%) interventions, while physical/occupational therapies showed lower effects (0.523 (52%); I² = 63.35%) and combined interventions showed borderline effects (0.593 (59%); I² = 19.96%); subgroup differences were significant (Q(5) = 22.63, p < 0.001). Regional effects were similar and non-significant across North America, Europe, and Asia. Meta-regression identified significant moderators including intervention context (Qₘ = 18.159, p = 0.020), outcome domain (Qₘ = 19.588, p = 0.003), age at intervention onset (Qₘ = 17.795, p = 0.003), and intervention category (Qₘ = 31.714, p < 0.001), while follow-up and intervention duration were not significant. Bayesian subgroup analyses confirmed strongest evidence for pharmacological, behavioral, and digital interventions. Overall, ASD interventions demonstrated a moderate and statistically significant overall effect (~0.50-0.62 (50-62%)), with substantial heterogeneity driven primarily by intervention type, context, and participant characteristics. Findings were consistent across frequentist, Bayesian, and sensitivity analyses, supporting robust but context-dependent effectiveness.
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