Frailty, Nutrition, and Quality of Life in Urban-Dwelling Older Adults Facing Vulnerability: Observational Study in Primary Heath Care
Bibliographic record
Abstract
Objective: To analyze the association between vulnerability, nutritional aspects, frailty, and quality of life (QoL) in older adults receiving care from Primary Health Care (PHC) services in an urban area. Methods: This was a cross-sectional study conducted with community-dwelling older adults. The following instruments were used to assess the variables of interest: the Vulnerable Elders Survey (VES-13), Mini Nutritional Assessment (MNA), Edmonton Frailty Scale (EFS), and the Medical Outcomes Study Short Form-36 (SF-36). Statistical procedures included association analyses, Spearman’s correlation, and binary logistic regression to identify predictors of the outcome. Results: A total of 323 individuals participated in the study, of whom 148 (45.8%) were classified as vulnerable according to the VES-13. Vulnerability showed a moderate negative correlation with nutritional status (r = -0.38; p < 0.001). Frailty (EFS) and quality of life (SF-36) showed strong correlations, especially in domains related to physical and functional aspects (r > 0.49; p < 0.050). Binary logistic regression revealed frailty (EFS) as the main predictor of vulnerability (R² = 0.20; p < 0.001; OR = 1.35 [95% CI: 1.24–1.48]), with functional independence (R² = 0.25; p < 0.001; OR = 3.96 [95% CI: 2.74–5.73]) and functional performance (R² = 0.17; p < 0.001; OR = 3.21 [95% CI: 2.21–4.67]) being the domains that most strongly increased the odds of vulnerability. Conclusion: There was a significant association between vulnerability, nutritional status, frailty, and quality of life. More refined analyses highlighted frailty, particularly in its functional domains, as a key predictor of vulnerability among older adults.
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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) | 0.001 | 0.001 |
| 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 | 0.001 | 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".