Patient, Treatment, and Health Care Utilization Variables Associated with Adherence to Metabolic Monitoring Practices in Children and Adolescents Taking Second-Generation Antipsychotics
Bibliographic record
Abstract
Objective:Children and adolescents with a range of psychiatric disorders are increasingly being prescribed atypical or second-generation antipsychotics (SGAs). While SGAs are effective at treating conduct and behavioural symptoms, they infer significant cardiometabolic risk. This study aims to explore what patient, treatment, and health care utilization variables are associated with adherence to Canadian Alliance for Monitoring Effectiveness and Safety of Antipsychotics in Children (CAMESA) metabolic monitoring guidelines.Method:A retrospective chart review of 294 children and adolescents accessing a large outpatient psychiatry setting within a 2-year study period (2014-2016) was conducted. Baseline and follow-up metabolic monitoring, demographic, treatment, and health care utilization variables were then assessed over a 1-year period of interest.Results:Metabolic monitoring practices did not adhere to CAMESA guidelines and were very poor over the 1-year observation period. There were significant differences between children (ages 4-12 years, <i>n</i> = 99) and adolescents (ages 13-18 years, <i>n</i> = 195). In adolescents, factors associated with any baseline metabolic monitoring were a higher number of psychiatry visits (odds ratio [OR], 1.2; 95% confidence interval [CI], 1.10 to 1.41), longer duration of contact (OR, 14; 95% CI, 2.31 to 82.4), and use of other non-SGA medications (OR, 3.2; 95% CI, 1.17 to 8.94). Among children, having an emergency room visit (OR, 3.4; 95% CI, 1.01 to 11.71) and taking aripiprazole (OR, 7.4; 95% CI, 2.02 to 27.45) increased the odds of receiving baseline metabolic monitoring.Conclusion:Findings from this study highlight the need for better metabolic monitoring for children and adolescents taking SGAs. Enhanced focus on opportunities for multidisciplinary collaboration is needed to improve the quality of care offered to this population.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".