Do Malnutrition, Sarcopenia and Frailty Overlap in Nursing-Home Residents?
Bibliographic record
Abstract
OBJECTIVES: To study the prevalence and overlap between malnutrition, sarcopenia and frailty in a selected group of nursing home (NH) residents. DESIGN: Cross-sectional descriptive study. SETTING: Nursing homes (NH). PARTICIPANTS: 92 residents taking part in an exercise and oral nutritional supplementation study; >75 years old, able to rise from a seated position, body mass index ≤30 kg/m2 and not receiving protein-rich oral nutritional supplements. MEASUREMENTS: The MNA-SF and Global Leadership Initiative on Malnutrition (GLIM) criteria were used for screening and diagnosis of malnutrition (moderate or severe), respectively. Sarcopenia risk was assessed by the SARC-F Questionnaire (0-10p; ≥4=increased risk), and for diagnosis the European Working Group of Sarcopenia in Older People (EWGSOP2) criteria was used. To screen for frailty the FRAIL Questionnaire (0-5p; 1-2p indicating pre-frailty, and >3p indicating frailty), was employed. RESULTS: Average age was 86 years; 62% were women. MNA-SF showed that 30 (33%) people were at risk or malnourished. The GLIM criteria verified malnutrition in 16 (17%) subjects. One third (n=33) was at risk for sarcopenia by SARC-F. Twenty-seven (29%) subjects displayed confirmed sarcopenic according to EWGSOP2. Around 50% (n=47) was assessed as pre-frail or frail. Six people (7%) suffered from all three conditions. Another five (5%) of the residents were simultaneously malnourished and sarcopenic, but not frail, while frailty coexisted with sarcopenia in 10% (n=9) of non-malnourished residents. Twenty-nine (32%) residents were neither malnourished, sarcopenic nor frail. CONCLUSIONS: In a group of selected NH residents a majority was either (pre)frail (51%), sarcopenic (29%) or malnourished (17%). There were considerable overlaps between the three conditions.
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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 | 0.000 |
| 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 | 0.001 |
| 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".