Survey of Atypical Antipsychotic Prescribing by Canadian Child Psychiatrists and Developmental Pediatricians for Patients Aged under 18 Years
Bibliographic record
Abstract
OBJECTIVE: To describe self-reported patterns of prescribing atypical antipsychotics (ATAs) and monitoring practices of child psychiatrists and developmental pediatricians in Canada. METHOD: We surveyed members of the Canadian Academy of Child and Adolescent Psychiatry and members of the Developmental Paediatrics Section of the Canadian Paediatric Society regarding the types and frequencies of ATAs they prescribed, the ages and diagnoses of patients for whom they prescribed these medications, and the types and frequencies of monitoring used. RESULTS: Ninety-four percent of the child psychiatrists (95% CI, 90% to 97%) and 89% of the developmental pediatricians (95% CI, 75% to 96%) prescribed ATAs, most commonly risperidone (69%). Diagnoses included psychotic, mood, anxiety, externalizing, and pervasive developmental disorders. Prescribing for symptoms such as aggression, low frustration tolerance, and affect dysregulation was also common. Twelve percent of all prescriptions were for children under age 9 years. Most clinicians monitored patients, but there were wide variations in the type and frequency of tests performed. CONCLUSIONS: Despite the lack of formal indications, ATAs were prescribed by this group of clinicians for many off-label indications in youth under age 18 years, including very young children. Neither evidence-based guidelines nor a consensus on monitoring exist for this age group.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".