Atypical Antipsychotics for Irritability in Pediatric Autism: A Systematic Review and Network Meta-Analysis
Bibliographic record
Abstract
OBJECTIVE: Irritability is common in pediatric autism spectrum disorder (ASD) patients. This can have major implications in child development, receptivity to behavioral therapy, as well as child and caregiver well-being. A systematic review and network meta-analysis were conducted to assess the efficacy and safety of atypical antipsychotics in treating irritability in these patients. METHODS: Studies were identified from Medline, Embase, and PsycINFO from inception to March 2018. The clinical trials database was reviewed. Studies were included if they were a double-blind, randomized controlled trial utilizing the Aberrant Behavior Checklist Irritability (ABC-I) to measure the efficacy of atypical antipsychotic monotherapy. Data extraction was carried out following the Preferred Reporting Items for Systematic Reviews and Meta-analyses for network meta-analysis guidelines. The main outcome was the reduction in irritability score using the ABC-I subscale from baseline. RESULTS: Eight trials comparing four interventions-risperidone, aripiprazole, lurasidone, and placebo in 878 patients, were included. Both risperidone and aripiprazole had significantly reduced ABC-I scores than placebo. Estimates of mean differences (95% credible intervals) were risperidone, -6.89 (-11.14, -2.54); aripiprazole, -6.62 (-10.88, -2.22); and lurasidone, -1.61 (-9.50, 6.23). Both risperidone and aripiprazole had similar safety. There were only eight studies included in the analysis, however, sample sizes were not small. Variance in reporting of adverse effects limited the quality of safety analysis. CONCLUSION: Risperidone and aripiprazole were the two best drugs, with comparable efficacy and safety in pediatric ASD patients. These two medications could be beneficial in improving irritability in these patients.
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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 | 0.016 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 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".