Impact of agitation in long‐term care residents with dementia in the United States
Bibliographic record
Abstract
OBJECTIVES: To describe characteristics and compare clinical outcomes including falls, fractures, infections, and neuropsychiatric symptoms (NPS) among long-term care residents with dementia with and without agitation. METHODS: A cross-sectional secondary analysis of administrative healthcare data was conducted whereby residents with dementia residing in a long-term care facility for ≥12 months were identified from the AnalytiCare LLC database (10/2010-06/2014) and were classified into mutually exclusive cohorts (Agitation Cohort or No-Agitation Cohort) based on available agitation-related symptoms. Entropy balancing was used to balance demographic and clinical characteristics between the two cohorts. The impact of agitation on clinical outcomes was compared between balanced cohorts using weighted logistic regression models. RESULTS: The study included 6,265 long-term care residents with dementia among whom, 3,313 were included in the Agitation Cohort and 2,952 in the No-Agitation Cohort. Prior to balancing, residents in the Agitation Cohort had greater dementia-related cognitive impairment and clinical manifestations compared to the No-Agitation Cohort. After balancing, residents with and without agitation, respectively, received a median of five and four distinct types of medications (including antipsychotics). Further, compared to residents without agitation, those with agitation were significantly more likely to have a recorded fall (OR = 1.58), fracture (OR = 1.29), infection (OR = 1.18), and other NPS (OR = 2.11). CONCLUSIONS: Agitation in long-term care residents with dementia was associated with numerically higher medication use and an increased likelihood of experiencing falls, fractures, infections, and additional NPS compared to residents without agitation, highlighting the unmet need for effective management of agitation symptoms in this population.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".