Psychotropic prescribing rates and pharmacogenomic testing implications for autism in the Canadian primary care sentinel surveillance network
Bibliographic record
Abstract
OBJECTIVE: To estimate prescribing rates of psychotropic drugs to individuals with autism and the proportion of these individuals who could benefit from pharmacogenetic testing. METHODS: Prescribing data for 92 psychotropic drugs, including 31 antidepressants, 22 antipsychotics, 14 mood stabilizer/antiepileptics, 17 anxiolytic/hypnotics and eight antiadrenergic/psychostimulant were retrieved from medical records of 787 (613 males) autistic individuals who sought treatment from a primary care office enrolled in the Canadian Primary Care Sentinel Surveillance Network between 2012 and 2014. Each prescribed drug was cross-referenced with pharmacogenomic-based prescribing guidelines published by the Clinical Pharmacogenetics Implementation Consortium, the Dutch Pharmacogenetics Working Group, and the Canadian Pharmacogenomics Network for Drug Safety. RESULTS: More than half (58%) of the participants were prescribed a psychotropic drug and 37% were prescribed two or more psychotropic drugs concurrently. Among the 83 psychotropic drugs examined, 54 (65%) were prescribed to one or more participants during the study's observation period. The ten most frequently prescribed psychotropics were methylphenidate (16.3%), risperidone (12.8%), lorazepam (12.1%), fluoxetine (7.9%), sertraline (7.1%), quetiapine (6.9%), aripiprazole (6.1%), lisdexamfetamine (5.8%), citalopram (5.6%) and clonazepam (4.8%). Seventeen (32%) of the 54 psychotropic drugs prescribed were linked to a pharmacogenomic-based prescribing guideline, including risperidone, sertraline, aripiprazole and citalopram. CONCLUSIONS: Our findings suggest primary care providers in Canada prescribe a wide range of psychotropics to their patients with autism, some of which may benefit from the integration of pharmacogenomic information into their treatment planning.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".