Dispensed prescriptions for quetiapine and other second-generation antipsychotics in Canada from 2005 to 2012: a descriptive study
Bibliographic record
Abstract
BACKGROUND: The use of antipsychotic drugs, particularly quetiapine, has increased at an unprecedented rate in the last decade, primarily in relation to nonpsychotic indications. This increased use is concerning because of the high rates of metabolic and extrapyramidal side effects and inadequate monitoring of these complications. The purpose of this study was to measure the use of quetiapine and other second-generation antipsychotics by primary care physicians and psychiatrists and the most common diagnoses associated with quetiapine recommendations. METHODS: We analyzed data on antipsychotic use from the IMS Brogan Canadian CompuScript Database and the Canadian Disease and Treatment Index, with a focus on quetiapine. We looked at the number of dispensed prescriptions for second-generation antipsychotics written by primary care physicians and psychiatrists and the diagnoses associated with recommendations for quetiapine from 2005 to 2012. RESULTS: Between 2005 and 2012, there was a 300% increase in dispensed prescriptions for quetiapine ordered by family physicians: from 1.04 million in 2005 to 4.17 million in 2012. In comparison, dispensed prescriptions from family physicians for risperidone increased 37.4%: from 1.39 million in 2005 to 1.91 million in 2012; those for olanzapine increased 37.1%, from 0.97 million in 2005 to 1.33 million in 2012. Dispensed prescriptions for quetiapine ordered by psychiatrists increased 141.6%: from 0.87 million in 2005 to 2.11 million in 2012. The top 4 diagnoses associated with quetiapine in 2012 were mood disorders, psychotic disorders, anxiety disorders and sleep disturbances. A 10-fold increase in quetiapine recommendations for sleep disturbances was seen over the study period, with almost all coming from family physicians. INTERPRETATION: These findings indicate a preferential increase in the use of quetiapine over other antipsychotic drugs and show that most of the increased use is a result of off-label prescribing by family physicians.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".