Validity of the Malnutrition Universal Screening Tool for Evaluation of Frailty Status in Older Hospitalised Patients
Bibliographic record
Abstract
The malnutrition-universal-screening-tool (MUST) is commonly used for screening malnutrition in hospitalised patients but its utility in the detection of frailty is unknown. This study determined the utility of MUST in detection of frailty in older hospitalised patients. This prospective-study enrolled 243 patients ⩾65 years in a tertiary-teaching hospital in Australia. Patients with a MUST score of ⩾1 were classified as at-risk of malnutrition. Frailty status was determined by the Edmonton-Frail-Scale (EFS) and patients with an EFS score of >8 were classified as frail. We validated the MUST against the EFS by plotting a receiver-operating-characteristic-curve (ROC) curve and area-under-the-curve (AUC) was determined. The mean (SD) age was 83.9 (6.5) years and 126 (51.8%) were females. The EFS determined 149 (61.3%) patients as frail, while 107 (44.1%) patients were at-risk of malnutrition according to the MUST. There was a positive linear but weak association between the MUST and the EFS scores (Pearson’s correlation coefficient= .22, 95% CI .12– .36, p < .001). The sensitivity, specificity, positive and negative predictive value of MUST in the detection of frailty was 51%, 67%, 78.5% and 37%, respectively and the AUC was .59 (95% CI .53–.65, p < .001). The MUST is moderately sensitive in detection of frailty in older-hospitalised patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".