Nutritional, Cognitive, and Functional Deficits, Frailty, and Quality of Life Associated With Fall Risk in Community-Dwelling Older Adults: A Cross-Sectional Study Conducted in Brazil
Bibliographic record
Abstract
INTRODUCTION/OBJECTIVE: Falls affect approximately 30% of the older adult population. We aimed to compare the associations between fall risk and different multidimensional health aspects among older adults receiving care in the Brazilian Primary Health Care (PHC) system. METHOD: Cross-sectional, quantitative study involving older adults from PHC. The Fall Risk Score, Mini Nutritional Assessment, Mini-Mental State Examination, Edmonton Frail Scale, Barthel Index, Lawton & Brody Scale, and Medical Outcomes Study Questionnaire Short Form was used to measure the variables of interest. Correlation analyses and binary logistic regression were also employed. RESULTS: A total of n = 257 individuals participated, of whom n = 102 (39.7%) were with risk for falls. Preserved cognition, absence of frailty, and better functionality levels were identified as protective factors against fall risk through association and correlation analyses. The binary logistic regression analysis found that the factors contributing most to the reduction of fall risk were higher nutritional scores, better cognitive function, preserved functionality (BADL and IADL), and the functional domain of quality of life (QoL). CONCLUSION: Better nutritional status, cognition, functionality, and QoL were associated with a lower risk of falls. Although frailty exhibited similar results, it did not stand out equally as a contributing factor to fall risk.
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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".