The Pharmacoepidemiology of Antipsychotic Medications for Canadian Children and Adolescents: 2005–2009
Bibliographic record
Abstract
OBJECTIVE: To describe the frequency and trends of use of antipsychotics in Canadian children and adolescents from 2005 to 2009. METHODS: We performed analyses on the IMS Brogan databases on drug recommendation (prescribed drug) frequency, reasons for recommendations (therapeutic indication), and duration of use of antipsychotics, psychostimulants, and selective serotonin reuptake inhibitors (SSRIs) for Canadian children between 2005 and 2009. Changes in the number of and reasons for drug recommendations over the 5-year period are highlighted by using descriptive statistics. RESULTS: Antipsychotic drug recommendations by all specialists for children increased by 114% from 2005 to 2009. In contrast, psychostimulant and SSRI drug recommendations increased by 36% and 44%, respectively. The majority of antipsychotic drug recommendations were for second-generation antipsychotics (SGAs). Attention-deficit/hyperactivity disorder (ADHD) was the most common therapeutic indication associated with an SGA recommendation. Antipsychotic drug recommendations for children with ADHD more than tripled between 2005 and 2009. The average duration of antipsychotic use in children varied by drug and age group. For risperidone, median duration of use was 90 days in children aged 1-6, 180 days in children aged 7-12, and 200 days in children aged 13-18. CONCLUSION: The use of antipsychotics in Canadian children greatly increased from 2005 to 2009. Comparison of antipsychotic data with psychostimulant and SSRI data shows that there has been a disproportionate increase in antipsychotic use over this interval, despite the lack of approval for pediatric use by Health Canada. Postmarketing surveillance activities of antipsychotic use in children appear appropriate.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".