Second Generation Antipsychotics in Asperger’s Disorder and High Functioning Autism: A Systematic Review of the Literature and Effectiveness of Meta-Analysis.
Bibliographic record
Abstract
OBJECTIVE: Second generation antipsychotics (SGA) have gained increased evidence for the treatment of irritability and aggression in children and adolescents with lower functioning autistic disorder. Individuals with Asperger's Disorder (AD) and High Functioning Autism (HFA) experience significant emotional and behavioral problems and psychiatric comorbidity. There is a need to review the published literature on SGA treatment efficacy in the AD and HFA populations to provide more effective treatment choices for these subgroups. METHODS: We conducted a systematic review and meta-analysis of the recent English literature on SGA use in children and adolescents (ages 0-24 years) with AD and HFA using the Medline/PubMed and PsychINFO computerized databases. Key search words were 'Asperger', 'high functioning autism', 'autism spectrum disorders (ASD)', and 'pervasive developmental disorder (PDD)' in combination with 'second generation antipsychotics', 'aripiprazole; 'olanzapine', 'quetiapine', 'risperidone', or 'ziprasidone'. RESULTS: Our search yielded 214 citations, however only open-label or randomized-controlled trials (RCT) with ≥25% of their subjects having an IQ≥71 were included in our review. Eleven original studies met our inclusion parameters for review; eight studies for the meta-analysis. These studies, although limited in methodological rigor, and the meta-analytic results suggest that SGAs provide improvement in behavioral symptoms associated with AD and HFA. The majority of the studies reported weight gain as a potentially concerning adverse effect. CONCLUSION: There is a lack of robustly conducted trials on the use of SGAs in the management of AD and HFA. More research in pharmacological and psychosocial treatments is warranted. Clinicians are cautioned to approach pharmacological treatment prudently balancing benefit with potential cardiometabolic risk.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".