Prevalence of frailty and its associated factors in older hospitalised patients in Vietnam
Bibliographic record
Abstract
BACKGROUND: Frailty is an emerging issue in geriatrics and gerontology. The prevalence of frailty is increasing as the population ages. Like many developing countries, Vietnam has a rapidly ageing population. However, there have been no studies about frailty in older people in Vietnam. This study aims to investigate the prevalence of frailty and its associated factors in older hospitalised patients at the National Geriatric Hospital in Hanoi, Vietnam. METHODS: Prospective observational study in inpatients aged ≥60 years at the National Geriatric Hospital in Hanoi, Vietnam from 4/2015 to 10/2015. Frailty was assessed using the Reported Edmonton Frail Scale (REFS) and Fried frailty phenotype. RESULTS: A total of 461 patients were recruited (56.8% female, mean age 76.2 ± 8.9 years). The prevalence of frailty was 31.9% according to the REFS. Using the Fried frailty criteria, the percentages of non-frail, pre-frail and frail participants were 24.5, 40.1 and 35.4%, respectively. Factors associated with frailty defined by REFS were age (OR 1.05 per year, 95% CI 1.03-1.08), poor reported nutritional status (OR 4.51, 95% CI 2.15-9.44), and not finishing high school (OR 2.18, 95% CI 1.37-3.46). Factors associated with frailty defined by the Fried frailty criteria included age (OR 1.07 per year, 95% CI 1.05-1.10), poor reported nutritional status (OR 2.96, 95%CI 1.43-6.11), not finishing high school (OR 1.58, 95% CI 1.01-2.46) and cardiovascular disease (OR 1.76, 95% CI 1.16-2.67). CONCLUSIONS: While further studies are needed to examine the impact of frailty on outcomes in Vietnam, the observed high prevalence of frailty in older inpatients is likely to have implications for health policy and planning for the ageing population in Vietnam.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".