Off-label use of antipsychotics and associated factors in community living older adults
Bibliographic record
Abstract
BACKGROUND: Given the common off-label use of antipsychotics (AP), we aimed to assess the factors associated with this use in community living older adults. METHODS: The study sample consisted of a large representative sample of older adults (n = 4108), covered under a public drug insurance plan in Canada. Off-label use of antipsychotics was defined by the absence of an approved indication for this use, according to Health Canada's drug product database. Multinomial logistic regression was used to assess the factors associated with off-label use. RESULTS: The prevalence of antipsychotics use was 2.5%, of which 78% was off-label. Compared to non-use, off-label antipsychotics use was negatively associated with advanced age (≥75 vs. 65-74 years old) (OR: 0.46; 95%CI: 0.27-0.78); and positively associated with higher education level (OR: 2.68; 95% CI: 1.64-4.40), higher number of outpatient visits (≥6) (OR: 2.39; 95%CI: 1.34-4.25), antidepressant or benzodiazepine use (OR: 5.81; 95%CI: 3.31-10.21), and the presence of an organic brain syndrome & Alzheimer's (OR: 5.73; 95%CI: 1.74-18.89). Compared to labeled use, off-label use was less likely in those with major depression (OR: 0.02; 95%CI: <0.01-0.11) and with insomnia (OR: 0.13; 95%CI: 0.02-0.91). CONCLUSIONS: The majority of antipsychotics prescribed to community living older adults were off-label. This off-label use was more likely in complex clinical cases with multiple outpatient visits and other psychotropic drugs use. Further research should focus on the long-term effects associated with off-label use of antipsychotics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".