The overlap of frailty and malnutrition in older hospitalised patients: An observational study.
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Frailty and malnutrition are geriatric syndromes with common risk-factors. Limited studies have investigated these two conditions simultaneously in hospitalised patients. This study investigated the overlap of frailty and malnutrition in older hospitalised patients. METHODS AND STUDY DESIGN: This prospective study enrolled 263 patients ≥65 years in a tertiary-teaching hospital in Australia. Frailty status was assessed by use of the Edmonton-Frail-Scale (EFS) and malnutrition risk was determined by use of the Malnutrition Universal Screening Tool (MUST). Patients were divided into four categories for comparison: normal, at malnutrition- risk only, frail-only and both frail and at malnutrition risk. Multivariable regression models compared clinical outcomes: length of hospital stay (LOS), in-hospital mortality, health-related quality of life (HRQoL) and 30- day readmissions after adjustment for age, sex, Charlson comorbidity index (CCI) and living-status. RESULTS: The mean (SD) age was 84.1 (6.6) years and 51.2% were females. The prevalence of patients who were at malnutrition- risk only was 14.8%, frailty only 27.8% and 33.5% were both frail and at malnutrition-risk. Frail-only patients were more likely to be older, from a nursing home and with a higher CCI than malnourished only patients. Frail patients had a worse HRQoL (coefficient -0.08, 95% -0.0132--0.031, p=0.002) and were more likely to have a longer LOS (coefficient 5.91, 95% CI 0.77-11.14, p=0.024) than patients at-risk of malnutrition. Other clinical outcomes were similar between the two groups. CONCLUSIONS: There is a substantial overlap of frailty and malnutrition in older hospitalised patients and frailty is associated with worse clinical outcomes than malnutrition.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".