Adherence to antipsychotic laboratory monitoring guidelines in children and youth: a population-based study
Bibliographic record
Abstract
Background: In 2011, the Canadian Alliance for Monitoring Effectiveness and Safety of Antipsychotics in Children (CAMESA) published guidelines for the metabolic monitoring of antipsychotic-treated children and youth. Population-based studies examining adherence to these guidelines are needed to ensure the safe use of antipsychotics in children and youth. Methods: We conducted a population-based study of all Ontario residents aged 0 to 24 who were newly dispensed an antipsychotic between April 1, 2018, and March 31, 2019. We estimated prevalence ratios (PRs) and 95% confidence intervals (CI) associating sociodemographic characteristics with the receipt of baseline and follow-up (3- and 6-month) laboratory testing using log-Poisson regression models. Results: Overall, 6,505 of 27,718 (23.5%) children and youth newly dispensed an antipsychotic received at least one guideline-recommended baseline test. Monitoring was more prevalent among individuals aged 10 to 14 years (PR 1.20; 95% CI 1.04 to 1.38), 15 to 19 years (PR 1.60; 95% CI 1.41 to 1.82), and 20 to 24 years (PR 1.71; 95% CI 1.50 to 1.94) compared to children under the age of 10. Baseline monitoring was associated with mental health-related hospitalizations or emergency department visits in the year preceding therapy (PR 1.76; 95% CI 1.65 to 1.87), a prior diagnosis of schizophrenia (PR 1.20; 95% CI 1.14 to 1.26) or diabetes (PR 1.35; 95% CI 1.19 to 1.54), benzodiazepine use (PR 1.13; 95% CI 1.04 to 1.24), and receipt of a prescription from a child and adolescent psychiatrist or developmental pediatrician versus a family physician (PR 1.41; 95% CI 1.34 to 1.48). Conversely, monitoring was less frequent in individuals co-prescribed stimulants (PR 0.83; 95% CI 0.75 to 0.91). The prevalence of any 3- and 6-month follow-up monitoring among children and youth receiving continuous antipsychotic therapy at these time points was 13.0% (1,179 of 9,080) and 11.4% (597 of 5,261), respectively. Correlates of follow-up testing were similar to those of baseline monitoring. Conclusion: Most children initiating antipsychotic therapy do not receive guideline-recommended metabolic laboratory monitoring. Further research is needed to understand reasons for poor guideline adherence and the role of clinician training and collaborative service models in promoting best monitoring practices.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".