A Population-Based Study of Antipsychotic Prescription Trends in Children and Adolescents in British Columbia, from 1996 to 2011
Bibliographic record
Abstract
OBJECTIVES: To establish prevalence rates of antipsychotic (AP) prescriptions for children 18 years of age or younger in British Columbia (BC) from 1996 to 2011 by age, sex, AP type, and primary diagnosis; and to identify the predominant AP prescribers for children by specialty training. METHODS: BC Ministry of Health administrative data were used to describe AP prescriptions for youth aged 18 years or younger. Comparisons were made using population prevalence based on sex; age group; AP; International Classification of Diseases, Ninth Revision, diagnosis; and prescriber specialty. RESULTS: From 1996 to 2011, overall AP (both first and second generation) prescription prevalence rate increased 3.8-fold (1.66 to 6.37 per 1000 population); second-generation AP (SGA) prescriptions increased 18.1-fold (0.33 to 5.98 per 1000 population). The highest increase in all AP prescriptions occurred in males aged 13 to 18 years (3.3 to 14.4 per 1000 population; 4.4-fold), followed by similar increases in males aged 6 to 12 years (2.3 to 8.6 per 1000 population; 3.7-fold) and in females aged 13 to 18 years (2.8 to 10.7 per 1000 population; 3.8-fold). Overall, the 3 most common diagnoses associated with all AP prescriptions were depressive disorders (12.8%), hyperkinetic syndrome of childhood (11.7%), and neurotic disorders (11.1%); however, variation was observed by prescriber specialty training. Among all new AP prescriptions in 2010/11, 38.6%, 34.3%, and 15.6% were provided by psychiatrists, family physicians, and pediatricians, respectively. CONCLUSIONS: There has been an exponential rise in SGA prescriptions in BC secondary to extensive off-label use, not only by psychiatrists but also by family physicians and pediatricians. Knowledge translation initiatives promoting evidence-based prescribing and monitoring practices related to SGA treatment need to target all 3 prescriber groups and be tailored for age subgroups.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| 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) | 0.002 | 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".