Harms of Antipsychotics in Children and Young Adults: A Systematic Review Update
Bibliographic record
Abstract
OBJECTIVE: To update and extend our previous systematic review on first- (FGAs) and second-generation antipsychotics (SGAs) for treatment of psychiatric and behavioral conditions in children, adolescents, and young adults (aged ≤24 years). This article focuses on the evidence for harms. METHOD: We searched (to April 2016) 8 databases, gray literature, trial registries, Food and Drug Administration reports, and reference lists. Two reviewers conducted study screening and selection independently, with consensus for selection. One reviewer extracted and another verified all data; 2 reviewers independently assessed risk of bias. We conducted meta-analyses when appropriate and network meta-analysis across conditions for changes in body composition. Two reviewers reached consensus for ratings on the strength of evidence for prespecified outcomes. RESULTS: A total of 135 studies (95 trials and 40 observational) were included, and 126 reported on harms. FGAs caused slightly less weight gain and more extrapyramidal symptoms than SGAs. SGAs as a class caused adverse effects, including weight gain, high triglyceride levels, extrapyramidal symptoms, sedation, and somnolence. They appeared to increase the risk for high cholesterol levels and type 2 diabetes. Many outcomes for individual drug comparisons were of low or insufficient strength of evidence. Olanzapine caused more short-term gains in weight and body mass index than several other SGAs. The dose of SGAs may not make a difference over the short term for some outcomes. CONCLUSIONS: Clinicians need to weigh carefully the benefit-to-harm ratio when using antipsychotics, especially when treatment alternatives exist. More evidence is needed on the comparative harms between antipsychotics over the longer term.
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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 | 0.014 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".