Antipsychotic Therapy and Short-term Serious Events in Older Adults With Dementia
Bibliographic record
Abstract
BACKGROUND: Antipsychotic therapy is widely used to treat behavioral problems in older adults with dementia. Cohort studies evaluating the safety of antipsychotic therapy generally focus on a single adverse event. We compared the rate of developing any serious event, a composite outcome defined as an event serious enough to lead to an acute care hospital admission or death within 30 days of initiating antipsychotic therapy, to better estimate the overall burden of short-term harm associated with these agents. METHODS: In this population-based, retrospective cohort study, we identified 20 682 matched older adults with dementia living in the community and 20 559 matched individuals living in a nursing home between April 1, 1997, and March 31, 2004. Propensity-based matching was used to balance differences between the drug exposure groups in each setting. To examine the effects of antipsychotic drug use on the composite outcome of any serious event we used a conditional logistic regression model. We also estimated adjusted odds ratios using models that included all covariates with a standard difference greater than 0.10. RESULTS: Relative to those who received no antipsychotic therapy, community-dwelling older adults newly dispensed an atypical antipsychotic therapy were 3.2 times more likely (95% confidence interval, 2.77-3.68) and those who received conventional antipsychotic therapy were 3.8 times more likely (95% confidence interval, 3.31-4.39) to develop any serious event during the 30 days of follow-up. The pattern of serious events was similar but less pronounced among older adults living in a nursing home. CONCLUSIONS: Serious events, as indicated by a hospital admission or death, are frequent following the short-term use of antipsychotic drugs in older adults with dementia. Antipsychotic drugs should be used with caution even when short-term therapy is being prescribed.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".