Effect of regulatory warnings on antipsychotic prescription rates among elderly patients with dementia: a population-based time-series analysis
Bibliographic record
Abstract
BACKGROUND: Three warnings of serious adverse events associated with the use of atypical antipsychotic drugs among elderly patients with dementia were sent to health care professionals in Canada. We assessed the impact of these warnings on prescription rates of antipsychotic drugs in this patient population. METHODS: We used prescription drug claims data from Ontario to calculate prescription rates of atypical and conventional antipsychotic drugs among elderly patients with dementia from May 1, 2000, to Feb. 28, 2007. We performed a time-series analysis to estimate the effect of each warning on rates of antipsychotic drug use. RESULTS: Before the first warning, growth in the use of atypical antipsychotics was responsible for an increasing rate of overall antipsychotic use. Each warning was associated with a small relative decrease in the predicted growth in the use of atypical antipsychotic drugs: a 5.0% decrease after the first warning, 4.9% after the second and 3.2% after the third (each p < 0.05). The overall prescription rate of antipsychotic drugs among patients with dementia increased by 20%, from 1512 per 100 000 elderly patients in September 2002, the month before the first warning, to 1813 per 100 000 in February 2007, 20 months after the last warning. INTERPRETATION: Although the warnings slowed the growth in the use of atypical antipsychotic drugs among patients with dementia, they did not reduce the overall prescription rate of these potentially dangerous drugs. More effective interventions are necessary to improve postmarket drug safety in vulnerable populations.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".