Cholinesterase Inhibitor Use in U.S. Nursing Homes: Results from the National Nursing Home Survey
Bibliographic record
Abstract
OBJECTIVES: To determine the frequency of cholinesterase inhibitor (ChEI) use in nursing home (NH) residents with dementia and examine correlates of ChEI use in this population. DESIGN: Cross-sectional study using the 2004 National Nursing Home Survey (NNHS). SETTING: A representative, stratified, random sample of U.S. NHs. PARTICIPANTS: All NNHS participants aged 65 and older with a chart diagnosis of dementia. MEASUREMENTS: Bivariate analyses to compare characteristics of NH residents with dementia according to ChEIs status and multivariable logistic regression to identify independent correlates of ChEI use. RESULTS: Almost half (49.1%) of NNHS participants had dementia, and 30.0% of those with dementia were receiving ChEIs. Donepezil accounted for 71% of all ChEI prescriptions. Multivariable logistic regression showed that ChEI use was independently associated with younger age (odds ratio (OR)=0.42, 95% confidence interval (CI)=0.28-0.64, aged > or =95 vs 65-74), less activity of daily living impairment (OR=0.49, 95% CI=0.42-0.58, severe vs mild impairment), greater use of antipsychotics (OR=1.33, 95% CI=1.16-1.54) and antidepressants (OR=1.38, 95% CI=1.20-1.59), and residence in NHs with more beds (OR=1.52, 95% CI=1.07-2.16, > or =200 beds vs <50 beds). CONCLUSION: Approximately 30% of NH residents with dementia in U.S. NHs are treated with ChEIs. Functional impairment and medical comorbidity are common in ChEIs users, although users tend to be younger and less impaired than NH residents with dementia who are not receiving ChEIs. Further study is required to determine the optimum use of ChEI in NH populations.
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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 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".