Ten Years of Antipsychotic Prescribing to Children: A Canadian Population-Based Study
Bibliographic record
Abstract
OBJECTIVE: To report the prescribing of antipsychotics to the youth population of the Canadian province of Manitoba during the course of a decade. METHODS: Use of antipsychotics in children and adolescents (aged 18 years or younger) was described using data collected from the administrative health databases of Manitoba Health and the Statistics Canada census between the fiscal years of 1999 and 2008. RESULTS: The prevalence of antipsychotic use in this segment of the population increased with the introduction of the second-generation antipsychotics (SGAs) from 1.9 per 1000 in 1999 to 7.4 per 1000 in 2008. The male-to-female antipsychotic usage ratio increased from 1.9 to 2.7 as the male youth population represented the fastest-growing subgroup of antipsychotic users in the entire population of Manitoba. The total number of prescriptions also increased significantly despite the lack of approved indications in this population. Proportion of use remained equally split between high- and low-income users. More than 70% of antipsychotic prescriptions to children and adolescents were written by general practitioners. The most common diagnoses linked to antipsychotic use were attention-deficit hyperactivity disorder and conduct disorders. Use of antipsychotics in combination with methylphenidate increased from 13% to 43%. CONCLUSION: Extensive off-label use of SGAs has been observed in the youth population of Manitoba for treatment of aggressive behaviours across a range of diagnoses. It is important to monitor antipsychotic prescribing to children as more reports of significant adverse events associated with antipsychotics become available.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".