Incidence and predictors of excess disability in walking among nursing home residents with middle-stage dementia: a prospective cohort study
Bibliographic record
Abstract
BACKGROUND: Inability to walk compromises the well-being of the growing number of nursing home residents with dementia. The purpose of this study was to estimate the incidence and identify predictors of walking disability that may be remediable. METHODS: A cohort was followed fortnightly for a year in 15 nursing homes in western Canada. The study participants comprised 120 ambulatory residents with middle-stage Alzheimer's, vascular or mixed dementia. Standardized measures of potential predictors of disability included the Charlson Comorbidity Index, Global Deterioration Scale, and Professional Environment Assessment Protocol. Walking disability was defined as using a wheelchair to go to meals in the dining room. RESULTS: Incidence of walking disability was 40.8% (95% confidence interval (CI): 32.7-50.2). Approximately half of this (27.0%; 95% CI: 19.7-36.5) was excess disability. Residents with more advanced dementia and living in a less supportive nursing home environment experienced an increased hazard of walking disability (Hazard Ratio (HR): 2.1; 95% CI: 1.2-3.8 and HR: 2.4; 95% CI: 1.3-4.4 respectively). After adjusting for age, comorbidity and stage of dementia, predictors of excess disability in walking included using antidepressants (HR: 2.2; 95% CI: 1.02-4.6), and not using cognitive enhancers (HR: 2.6; 95% CI: 1.03-6.4). CONCLUSIONS: Over half of walking disability in nursing home residents with middle-stage dementia may be modifiable. Creating supportive environments, ensuring access to cognitive enhancer drugs, and preventing and treating depression and the adverse effects of antidepressants, may help to reduce walking disability and excess disability.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".