Influence of Alzheimer’s disease on the relationship between nutritional status and risk of fall
Bibliographic record
Abstract
Although malnutrition and risk of falls in the elderly have increased in recent years, uncertainties exist as to whether these conditions are associated after controlling for sociodemographic variables, body composition, metabolic condition, and Alzheimer’s disease (AD). This study aimed to analyze the association between nutritional status and risk of fall in the elderly population. Participants were matched by gender and age, after they had been grouped on the basis of diagnosis of AD. The risk of falls, nutritional status, and mental status were assessed using the Downton Fall Risk Score (FRS), Mini Nutritional Assessment (MNA), and Mini Mental State Evaluation (MMSE), respectively. Logistic regression models adjusted for the main confounders were used in the analyses. Among the 68 elderly individuals studied, participants who were malnourished or at risk of malnutrition were more likely to fall (odds ratio = 8.29; 95% confidence interval = 1.49-46.04) than those with normal nutritional status, regardless of gender, age, education, body composition, and metabolic condition. This association did not remain significant after adjustment for AD, a potential confounder in this association. Malnutrition or its risk was independently associated with high risk of fall; thus, malnutrition should be considered in the prevention of falls among the elderly 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 | 0.003 |
| 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".