PRE-FRAILTY IN OLDER ADULTS: PREVALENCE AND ASSOCIATED FACTORS
Bibliographic record
Abstract
ABSTRACT Objective: to analyze pre-frailty prevalence in older adults residing in the community and associated factors. Method: a cross-sectional study, carried out with 291 elderly people registered in Family Health Strategy units. Pre-frailty was measured using the Edmonton Frail Scale, and the other variables were measured using different instruments. Data were collected from June to August 2018. Data analysis was performed using the Mantel Haenszel chi-square test, Fisher’s test and Poisson multivariate regression. Results: pre-frailty prevalence was 69.42% (95% CI; 63.77%-74.66%). Factors associated with pre-frailty were: low education (PR=1.37; 95% CI: 1.11-1.71), dependence on basic (PR=1.39; 95% CI: 1.22-1.59) and instrumental activities of daily living (PR=1.58; 95% CI: 1.40-1.78), depressed mood (PR=1.58; 95% CI: 1.40-1.78). =1.53; 95% CI: 1.31 1.78), negative self-rated health (PR=1.39; 95% CI: 1.15-1.69), polypharmacy (PR=1.30; CI 95%: 1.13-1.50), and nutritional risk (PR=1.27; 95% CI: 1.09-1.46). Conclusion: pre-frailty prevalence was higher than that found in other studies that used the same instrument, and the variables associated with this outcome demonstrated the existence of a common phenomenon among older adults. These are important results, as they highlight the need for investment in research and preventive interventions on the clinical, functional and social conditions of this population. Furthermore, it is necessary to invest in professional training programs for the comprehensive care of older adults, especially with regard to frailty assessment and prevention.
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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 | 0.001 |
| 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".