Trends in the Use of Long-Acting Injectable Antipsychotics in the Province of Manitoba, Canada
Bibliographic record
Abstract
BACKGROUND: Long-acting injectable antipsychotics (LAIAs) have advantages over oral antipsychotics but are not widely used. We aimed to evaluate the impact of market entry of second-generation LAIAs on prescribing trends. METHODS: We used administrative health databases to describe trends in LAIA use from 1995 to 2015 in the Canadian province of Manitoba. Age- and sex-specific incident and prevalent use were determined using prescription dispensation records for the entire population. We used interrupted time series analysis with Poisson regression to estimate change in LAIA use attributable to the market entry of the second-generation LAIA risperidone. RESULTS: We observed 3380 prevalent LAIA users and 2375 incident users in our cohort. Long-acting injectable antipsychotic use was higher in males. Incidence proportions declined from 21.5 users per 100,000 in 1996 to 4.8 in 2004 and then climbed to 14.7 by 2015. First-generation LAIA use peaked at 94.6 prevalent users per 100,000 in 1998 but fell to 40.9 in 2015. Long-acting injectable antipsychotic use increased 1.4% per quarter after the market entry of risperidone long-acting injectable. CONCLUSIONS: Risperidone risperidone long-acting injectable market entry had a positive impact on LAIA prescribing.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 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".