Quetiapine use in adults in the community: a population-based study in Alberta, Canada
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to evaluate trends in prescribing of the second-generation antipsychotic medication quetiapine to adults in the province of Alberta from 2008 to 2013 through examination of dispensed prescriptions, and diagnoses associated with users of quetiapine in 2013. METHODS: We analysed administrative data from Alberta Health; the Alberta Pharmaceutical Information Network (PIN) Dispenses health data set, the Practitioner Payments (Fee-For-Service claims) health data set and the Population Registry health data set. These data sets allowed us to identify discrete quetiapine recipients for each calendar year from 2008 to 2013. To evaluate diagnoses associated with users of quetiapine, we evaluated diagnostic codes used by physicians in billings claims in 2013. RESULTS: Quetiapine use increased over the 6-year time period studied. In 2008, there were 16,087 unique quetiapine recipients in Alberta (7.2 per 1000). By 2013, there were 35,314 unique quetiapine recipients (13.3 per 1000). Use by women was higher than men at all time points. Depression was most common diagnosis associated with quetiapine recipients, which was present in 56% of users of quetiapine. Other common diagnoses associated with quetiapine use included neurotic disorders, bipolar disorder and sleep disturbances. CONCLUSIONS: The current study of quetiapine use in the province of Alberta provides confirmatory data of the increasing use of quetiapine for the treatment of depression and anxiety disorders. Safe and rational prescribing practices must be encouraged in light of the modest advantages of quetiapine over no treatment as an adjunctive treatment of major depression, and the known harms of this medication.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".