The prevalence of malnutrition (MUST and MNA-SF), frailty and physical disability and relationship with mortality in older care home residents
Bibliographic record
Abstract
Background & AimsCurrently, there is lack of universal consensus on the use of effective malnutrition screening tools. Although malnutrition, frailty and physical disability are interrelated and associated with mortality in older people, there is a paucity of research in care home settings. With a high co-prevalence of these conditions, understanding their interconnectedness can provide a holistic view of an older person's health condition. The purpose of this study was to examine the prevalence of malnutrition (and risk) frailty and physical disability among care home residents using different methods, as well as the associations between markers of malnutrition (MUST and MNA-SF), physical function (Barthel Index, BI), frailty (Edmonton Frailty Scale, EFS), and all-cause mortality in care home residents.MethodsIn Lincoln, UK, 508 residents from care homes underwent screening for malnutrition (MNA-SF and MUST), frailty (EFS), and physical function (BI) as part of standard comprehensive geriatric assessment (CGA) between November 2015 and January 2018. Prevalence of conditions were assessed and MNA-SF, MUST, EFS, and BI-specific survival in each category were compared using Kaplan-Meier survival analysis (KMSA) with log-rank test. Multivariable analyses were conducted using the Cox proportional hazard model to identify prognostic factors that were statistically significant in care home residents.ResultsThere was significant discordance between malnutrition risk measured by MUST and MNA-SF. The percentage of patients ‘at risk’/‘medium risk’ and ‘malnourished’/‘high risk’ was 25.3%/49.9% for MNA and for 19.6%/31.57% for MUST. The prevalence of frailty measured by EFS was high with the percentage of residents with severe frailty being 70.9%. Only 8.6% of patients were functionally independent. The association between malnutrition risk (MUST) and mortality was not significant. MNA-SF appeared to be a better tool at predicting mortality in older care home residents (p < 0.001). Furthermore, the association between frailty (EFS) and mortality was significant (p < 0.01).ConclusionsThis study found high levels of malnutrition, frailty, and disability among UK care home residents, and a discordance between MUST and MNA-SF scoring patterns. The MNA-SF and EFS were better predictors of mortality than MUST and BI, highlighting the need for sensitive tools in assessing malnutrition and frailty risks in this population.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| 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".