Evidence-based recommendations for monitoring safety of second-generation antipsychotics in children and youth.
Bibliographic record
Abstract
BACKGROUND: The use of antipsychotics, especially second-generation antipsychotics (SGAs), for children with mental health disorders in Canada has increased dramatically over the past five years. These medications have the potential to cause major metabolic and neurological complications with chronic use. OBJECTIVE: To synthesize the evidence for specific metabolic and neurological side effects associated with the use of SGAs in children, and provide evidence-based recommendations for the monitoring of these side effects. METHODS: A systematic review of controlled clinical trials of SGAs involving children was performed. Recommendations for monitoring SGA safety were made according to a classification scheme based on the GRADE (Grading of Recommendations Assessment, Development and Evaluation) system. When there was inadequate evidence, recommendations were based on consensus and expert opinion. A multidisciplinary consensus group reviewed all relevant evidence and reached consensus on the recommendations. RESULTS: The present guidelines provide evidence-based recommendations for monitoring SGA safety. The strength of recommendations for specific physical examination manoeuvres and laboratory tests are provided for each SGA medication at specific time points. CONCLUSION: Multiple randomized controlled trials evaluated the efficacy of many of the SGAs in paediatric mental health disorders. These benefits, however, are not without risks - both metabolic and neurological side effects occur in children treated with SGAs. The risk of weight gain, increased body mass index and abnormal lipid levels is greatest with olanzapine, followed by clozapine and quetiapine. The risk of neurological side effects of the treatment is greatest with risperidone, olanzapine and aripiprazole. Appropriate monitoring procedures for adverse effects will improve the quality of care of children treated with these medications.
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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 | 0.046 | 0.181 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.014 |
| Bibliometrics | 0.014 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.010 | 0.004 |
| Research integrity | 0.013 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".