Atypical antipsychotic drug use and falls among nursing home residents in Winnipeg, Canada
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study is to assess whether atypical antipsychotic drug (AAD) use is associated with increased risk of falling among older (≥65 years) nursing home (NH) residents. METHODS: We conducted a nested case-control study using Resident Assessment Instrument Minimum Data Set 2.0 (RAI-MDS(©)) for NHs to identify falls, and population-based administrative healthcare databases to measure drug use and other study covariates. Cases (n = 626) were NH residents in Winnipeg, Canada, who had a fall between 1 April 2005 and 31 March 2007, and were matched to four controls on age, sex, and length of NH stay (n = 2388). RESULTS: While the odds of falling were statistically greater for AAD users versus nonusers (OR = 1.6, 95% CI 1.1-2.3), this association was type and dose dependent. Compared to nonusers, the odds of falling were greater for high-dose (>150 mg/day) quetiapine users and for high-dose (>2 mg/day) risperidone users. On the other hand, olanzapine (regardless of dose), low-dose quetiapine, and low-dose risperidone use were not associated with increased fall risk. Furthermore, the effect of AAD use, in general, on the risk of falling was significantly greater for people with wandering problems (OR = 1.8, 95% CI 1.1-3.1). CONCLUSIONS: Our findings suggest greater risk of falling with high-dose quetiapine use and with high-dose risperidone use among NH residents. In addition, the effect of AAD use was greater for people who frequently wander. Further research is needed to confirm these findings, and to address other important unanswered questions about the safest dose and duration of AAD use.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".