Prescriptions for Alpha Agonists and Antipsychotics in Children and Youth with Tic Disorders: A Pharmacoepidemiologic Study
Bibliographic record
Abstract
Background: Trends in the use of antipsychotics and alpha agonists for the treatment of tic disorders in Canadian children, and how closely these trends align with evidence-based guidelines on the pharmacotherapy of tic disorders, have not been explored. Methods: IQVIA's Canadian Disease and Therapeutic Index, a survey-based data set, was used to identify prescription patterns by physicians. Respondents recorded all patient visits during a 48-hour period in each quarter of the year, including patient age, gender, drug recommendation and therapeutic indication. Recommendations for alpha agonists and antipsychotics from 2012 to 2016 were analysed for children and adolescents with tic disorders. Results: Risperidone and clonidine were the most commonly recommended medications for tic disorders over the study period, with 36,868 and 35,500 recommendations in 2016, respectively. Recommendations for clonidine increased over the study period, whereas those for risperidone decreased. Guanfacine (approved in Canada in 2013) was used less frequently than clonidine. Clonidine was more frequently recommended than antipsychotics in children younger than 6, in whom antipsychotic recommendations were uncommon. Aripiprazole was the second most commonly recommended antipsychotic for tic disorders, with 22,892 recommendations in 2016. Of the first-generation antipsychotics, pimozide was most commonly recommended (11,334 recommendations in 2016); haloperidol was infrequently recommended. Discussion: The trends observed are in line with guideline recommendations reflected in the decreasing use of risperidone, and the growing use of clonidine and guanfacine. The growing use of aripiprazole is likely due to emerging evidence from clinical trials supporting its efficacy for tics. Recommendations for pimozide and haloperidol were limited, likely due to the greater adverse effects associated with these medications.
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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 | 0.002 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".