Frailty, Nutrition, and Quality of Life in Urban‐Dwelling Older Adults Facing Vulnerability: Observational Study in Primary Heath Care Settings in Underpopulated Areas
Bibliographic record
Abstract
ABSTRACT To investigate the predictive relationships between frailty, nutritional factors, and Quality of Life (QoL) on the vulnerability of older adults enrolled in Primary Health Care (PHC) in an urban area. This was a cross‐sectional study conducted with community‐dwelling older adults. Participants receiving care in PHC in two Brazilian municipalities located in a sparsely populated region were recruited. The instruments used were Vulnerable Elders Survey (VES‐13), Mini Nutritional Assessment (MNA), Edmonton Frailty Scale (EFS), and Medical Outcomes Study Short Form‐36 (SF‐36). Association analyses, Spearman's correlation, and binary logistic regression were used. A total of 323 individuals were included. Binary logistic regression revealed frailty (EFS) as the main predictor of vulnerability ( R 2 = 0.20; p < 0.001; OR = 1.35 [95% CI: 1.24–1.48]), with functional independence ( R 2 = 0.25; p < 0.001; OR = 3.9 [95% CI: 2.74–5.73]) and functional performance ( R 2 = 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. Impaired nutrition showed a consistent predictive association ( R 2 = 0.11; p < 0.001; OR = 0.82 [95% CI: 0.76–0.89]). Frailty and poor nutritional status were predictors of vulnerability, with particular emphasis on physical‐functional aspects. QoL showed a moderate to strong correlation with vulnerability, especially in the physical domains.
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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".