First do no harm: promoting an evidence-based approach to atypical antipsychotic use in children and adolescents.
Bibliographic record
Abstract
OBJECTIVES: To review the evidence for efficacy and metabolic effects of atypical antipsychotics (AAPs), and to propose a metabolic monitoring protocol for AAP use in children and adolescents. METHODS: A PubMed search was performed to obtain all studies related to efficacy, metabolic side-effects, and monitoring in those less than 18 years of age. RESULTS: There are no approved indications for AAP use in children and adolescents in Canada. Based on US Food and Drug Administration approvals and a review of randomized controlled trials, we identified 7 indications for AAP use that target specific symptoms in youth including schizophrenia, bipolar I disorder, autism, pervasive developmental disorder, disruptive behaviour disorders (including conduct disorder and ADHD), developmental disabilities and Tourette Syndrome. A wide range of metabolic effects including weight gain, increased waist circumference, dysglycemia, dyslipidemia, hypertension, elevated hepatic transaminases and prolactin levels have been reported. We have developed a proposal for metabolic monitoring that includes anthropometric measurements and laboratory testing at baseline and appropriate intervals thereafter. CONCLUSION: There is an urgent need for national clinical practice guidelines that provide, not only appropriate treatment algorithms for AAP-use based on evidence, but also address metabolic monitoring and subsequent management of complications in this vulnerable population.
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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 | 0.127 | 0.232 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".