Nutritional risk assessed by the Malnutrition Universal Screening Tool as a predictor of frailty in acutely hospitalised older patients: An observational study.
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Frailty and malnutrition are overlapping geriatric syndromes and leads to poor clinical outcomes in older patients. This study determined whether Malnutrition Universal Screening Tool (MUST) can predict frailty in older hospitalised patients. METHODS AND STUDY DESIGN: This prospective study recruited 243 patients ≥65 years in a tertiary-teaching hospital in Australia. Frailty assessment was performed by use of the Edmonton-Frail-Scale (EFS), while malnutrition-risk was determined by use of the MUST. Patients with an EFS score >8 were classified as frail, while patients with a MUST score of 1 as at moderate malnutritionrisk and ≥2 as at high malnutrition-risk. Multivariable logistic regression determined whether malnutrition-risk predicts frailty after adjustment for various co-variates. RESULTS: The mean (SD) age was 83.9 (6.5) years) and 126 (51.9%) were females. One-hundred and forty-nine (61.3%) patients were classified as frail, while 66 (27.2%) were found to be at high malnutrition-risk according to the MUST. Frail patients were more likely to be older with a higher Charlson-index and on polypharmacy than non-frail patients. Patients who were at high malnutrition- risk were more likely to be living alone and on vitamin D supplementation than those at low malnutritionrisk. Patients who were at a high malnutrition-risk but not those who were at moderate malnutrition-risk, were more likely to be deemed frail (aOR 2.6, 95% CI 1.2-5.5, p=0.015) when compared to those who were at low malnutrition-risk. CONCLUSIONS: Only patients who were classified as at high malnutrition-risk according to the MUST are more likely to be deemed frail.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".