OLDER PEOPLE WITH SARCOPENIA IN VIETNAM: WHO ARE AT HIGHER RISK OF HAVING FRAILTY?
Bibliographic record
Abstract
This study aims to investigate the prevalence of frailty according to sarcopenic status, and predictor factors for frailty among participants having sarcopenia. This study is a secondary analysis from the baseline data of a study designed to investigate the prevalence of sarcopenia in older patients at the National Geriatric Hospital in Hanoi, Vietnam from 1/2018 to 7/2018. Sarcopenia was defined as having low lean mass plus low grip strength according to the criteria of the Foundation for the National Institutes of Health (FNIH). Frailty was defined by Fried’s frailty criteria. N=770, mean age 71.7 ± 8.8, 61.7% women. The prevalence of frailty among sarcopenic and non-sarcopenic participants were 26.2% and 4.7%, respectively. Sarcopenia was associated with a four-fold increased risk of frailty (adjusted OR 4.40, 95%CI 2.27–8.54), adjusted for age, gender, income, living alone, educational level, physical activity, nutritional status and comorbidity. Among participants with sarcopenia (n=385), predictor factors for frailty were advanced age (adjusted OR 1.05, 95%CI 1.01–1.09), low income (adjusted OR 19.10, 95%CI 2.85–127.96), malnutrition (adjusted OR 7.76, 95%CI 2.94–20.50), living in urban areas (adjusted OR 1.99, 95%CI 1.02–3.86), having any hospitalization in previous year (adjusted OR 2.35, 95%CI 1.11–4.95), having higher fall risk (adjusted OR 3.06, 95%CI 1.35–6.90) and higher Geriatric Depression Scale (adjusted OR 1.47,95%CI 1.31–1.65). Although sarcopenia has a strong impact on frailty, only around one-quarter of participants with sarcopenia were having frailty. The findings suggest further intervention study on social and mental support, nutrition and rehabilitation to prevent frailty in older people with sarcopenia in Vietnam.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".