A Longitudinal Treatment Effect Analysis of Antipsychotics on Behavior of Residents in Long-Term Care
Bibliographic record
Abstract
OBJECTIVE: The proportion of long-term care (LTC) residents being treated with antipsychotic medication is high, and these medications may exacerbate behavioral symptoms. We used propensity scores to investigate the effect of antipsychotic use on the worsening of behavioral symptoms among residents in LTC facilities. DESIGN: A retrospective study. SETTING AND PARTICIPANTS: Residents in LTC in 8 provinces and 1 territory in Canada, without severe aggressive behavior at baseline and reassessed at follow-up, between March 2000 and March 2022. METHODS: We used propensity score matching and weighting to balance baseline covariates and logistic regression to estimate the effect of antipsychotics on the worsening of behavioral symptoms in the original, matched, and weighted cohorts. The treatment variable was use of antipsychotic medication at baseline and the outcome was worsening of behavior at follow-up. RESULTS: A total of 494,215 participants were included [318,234 women and 175,981 men; mean age 82.8 years (SD 10.1; range 18-112)].130 558 (26.4%) used antipsychotics at baseline and 88,632 (17.9%) had worsening behavior in follow-up. In the matched cohort, there were 249,698 participants, and 124,849 were matched (1:1) in each treatment group. There was a significant association between antipsychotic use at baseline and worsening in behavior at follow-up in the adjusted regression models [OR 1.27 (95% CI 1.25-1.29), <0.0001] as well as in matched [OR 1.20 (95% CI 1.17-1.21), <0.0001] and weighted [OR 1.26 (95% CI 1.24-1.28), <0.0001] cohorts. CONCLUSIONS AND IMPLICATIONS: This study further evidence to support the cautious use of antipsychotics in LTC facilities. Future research in LTC facilities could include a more granular analyses of behavior change, including bidirectional analyses between different symptom severity classifications.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".