Enhancing metabolic monitoring for children and adolescents using second‐generation antipsychotics
Bibliographic record
Abstract
The prevalence of children and adolescents using second-generation antipsychotics (SGAs) has increased significantly in recent years. In this population, SGAs are used to treat mood and behavioural disorders although considered 'off-label' or not approved for these indications. Metabolic monitoring is the systematic physical health assessment of antipsychotic users utilized to detect cardiovascular and endocrine side effects and prevent adverse events such as weight gain, hyperglycaemia, hyperlipidemia, and arrhythmias. This practice ensures safe and efficacious SGA use among children and adolescents. Despite widely available, evidence-based metabolic monitoring guidelines, rates of monitoring continue to be suboptimal; this exposes children to the unnecessary risk of developing poor cardiovascular health and long-term disease. In this discursive paper, existing approaches to metabolic monitoring as well as challenges to implementing monitoring guidelines in practice are explored. The strengths and weaknesses of providing metabolic monitoring across outpatient psychiatry, primary care, and collaborative community settings are discussed. We suggest that there is no one-size-fits-all solution to improving metabolic monitoring care for children and adolescents using SGA in all settings. However, we advocate for a pragmatic global approach to enhance safety of children and adolescents taking SGAs through collaboration among healthcare disciplines with a focus on integrating nurses as champions of metabolic monitoring.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".