Selective Serotonin Reuptake Inhibitors for Children with Autism Spectrum Disorder: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Abstract Background Effects of Selective Serotonin Reuptake Inhibitors (SSRI) for symptoms of autism spectrum disorder (ASD) or comorbid conditions are uncertain. We conducted a systematic review and meta-analyses to determine the efficacy and safety of SSRIs in children with ASD on restricted repetitive behaviors, obsessive-compulsive, anxiety and depressive symptoms, disruptive behaviors, global functioning, and quality of life. Methods We searched MEDLINE, EMBASE, CENTRAL, CINAHL, PsycINFO, WHO ICTRP, Clinical.Trials.gov. to retrieve RCT of SSRIs versus placebo in children with ASD that evaluated the outcomes of interest with a validated measurement instrument. Two authors independently extracted data, assessed risk of bias and rated the certainty of evidence. Results Seven RCTs with a total of 606 participants were included. The evidence is very uncertain on the effect of SSRIs on restricted-repetitive behaviors and anxiety symptoms in children with ASD, there is low certainty of the evidence suggesting that SSRIs result in little to no effect on obsessive-compulsive symptoms and disruptive behaviors and there is moderate certainty of the evidence that SSRIs likely result in little to no difference in global functioning and a slight increase in adverse events. No studies evaluated depressive symptoms. Conclusions The number of studies on this population remains limited, and there is a lack of commonly accepted outcome measures able to identify relevant changes for patients and families. Protocol registration number:PROSPERO-CRD42020169836
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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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.031 |
| Bibliometrics | 0.006 | 0.006 |
| 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) | 0.006 | 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".