<p>The impact of frailty on prolonged hospitalization and mortality in elderly inpatients in Vietnam: a comparison between the frailty phenotype and the Reported Edmonton Frail Scale</p>
Bibliographic record
Abstract
AIMS: To investigate the impact of frailty on outcomes in older hospitalized patients, including prolonged length of stay and all-cause mortality 6 months after admission, using both the frailty phenotype and the Reported Edmonton Frail Scale (REFS). PATIENTS AND METHODS: This study is the follow-up phase of a study designed to investigate the prevalence of frailty and its impact on adverse outcomes in older hospitalized patients at the National Geriatric Hospital in Hanoi, Vietnam. RESULTS: A total of 461 participants were included, with a mean age 76.2±8.9 years, and 56.8% were female. The prevalence of frailty was 31.9% according to the REFS and 35.4% according to Fried's criteria. The kappa coefficient was 0.57 (95% CI =0.49-0.66) between the two frailty criteria in identifying frail and non-frail participants. There was a trend toward increasing the likelihood of prolonged hospitalization in participants with frailty defined by Fried's criteria (adjusted OR =1.49, 95% CI =0.94-2.35) or by REFS (adjusted OR =1.43, 95% CI =0.89-2.29). During 6 months of follow-up, 210 were lost and 18/251 (7.2%) participants died. Mortality was higher in those with frailty defined by either Fried's criteria or REFS. On multivariable survival analysis, adjusted HRs for mortality were 2.65 (95% CI =1.02-6.89) for Fried's criteria and 4.19 (95% CI =1.59-10.99) for REFS. CONCLUSION: Fried's frailty phenotype or REFS can be used as a screening tool to detect frailty in older inpatients in Vietnam and predict mortality. Frailty screening can help prioritize targeted frailty-tailored treatments, such as nutrition, early mobility and medication review, for these vulnerable patients to improve clinical outcomes.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".