Therapeutic effects of antidepressants for global improvement and subdomain symptoms of autism spectrum disorder: a systematic review and meta-analysis
Bibliographic record
Abstract
Background: No established pharmacological treatment is available for the core symptoms of autism spectrum disorder (ASD). This study aimed at investigating the efficacy of antidepressants for the core and associated symptoms of ASD. Methods: We searched PubMed, Embase, ClinicalKey, Cochrane CENTRAL, ScienceDirect, Web of Science and ClinicalTrials.gov using the keywords “ASD” and “antidepressants.” We searched from database inception to June 2021 for randomized controlled trials of antidepressant use in patients with ASD. We calculated pooled effect sizes based on a random-effects model. Results: Analysis of 16 studies with 899 participants showed improvements in restricted and repetitive behaviours (effect size = 0.27) and global symptoms (effect size = 1.0) in patients with ASD taking antidepressants versus those taking placebos ( p ≤ 0.01). We found no differences between the 2 groups ( p ≥ 0.36) in terms of dropout rate (odds ratio [OR] = 1.17) or rate of study discontinuation because of adverse events (OR = 1.05). We also noted improvements in irritability and hyperactivity in the antidepressant group (Hedges g = 0.33 and 0.22, respectively, both p < 0.03). Subgroup analyses showed significant effects of medication type (i.e., clomipramine was better than SSRIs) and age (antidepressants were more effective in adults than in children or adolescents) on both restricted and repetitive behaviours and global improvement ( p < 0.05). Meta-regression demonstrated that better therapeutic effects were associated with lower symptom severity and older age. Limitations: The small effect sizes and variations in treatment response that we found warrant further study. Conclusion: Our results supported the effectiveness of antidepressants for global symptoms and symptom subdomains of ASD, with tolerable adverse effects. Low symptom severity and adulthood were associated with better 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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.039 |
| Bibliometrics | 0.008 | 0.007 |
| 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".