Frailty and mortality: Utility of Frail-VIG index in ED short-stay units for older adults
Bibliographic record
Abstract
BACKGROUND: Frailty assessment allows the identification of patients at risk of death. The aim here was to study the ability of Frail-VIG Index (FI-VIG) in order to discriminate frailty groups of older adults and garner its correlation with mortality in an Emergency-Department Short-Stay Unit (ED-SSU). METHODS: Our observational, single-center, prospective study consecutively included patients over 65-years-old admitted between March 1, 2021, and April 30, 2021. RESULTS: 302 patients were included (56 % women), mean age 83 ± 8 years, and 39.1 % of them had a functional disability whilst 16.5 % of them had dementia. A total of 174 patients (58 %) met the frailty criteria (FI-VIG ≥ 0.2): 111 (63.8 %) had mild frailty (FI-VIG 0.2-0.36), 52 (29.9 %) had moderate frailty (FI-VIG 0.36-0.55), and 11 (6.3 %) had advanced frailty (FI-VIG > 0.55). Mortality at 30 days, 6 months, and 1 year was analyzed: no frailty was 6.3 %, 10.8 %, and 12.5 %, respectively; mild frailty was 10.8 %, 22.5 %, and 22.5 %, respectively; moderate frailty was 25 %, 34.6 %, and 42.3 %, respectively; advanced frailty was 36.4 %, 54.5 %, and 3.6 %, respectively. This shows the significant differences between the groups (1-year mortality p < 0.001). Mild frailty vs. non-frail HR was 2.47 (95 %CI 1.12-5.46), moderate frailty vs. non-frail HR was 6.93 (95 %CI 3.16-15.23), and advanced frailty vs. non-frail HR was 11.29 (95 %CI 3.54-36.03). The mean test time was 7 min. CONCLUSIONS: There was a strong correlation between frailty degree and mortality at 1, 6, and 12 months. FI-VIG is fast and easy-to-use in this setting. It is routine implementation in ED-SSUs could enable early risk stratification.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".