Screening Community-Living Older Adults for Protein Energy Malnutrition and Frailty: Update and Next Steps
Bibliographic record
Abstract
Abstract Protein-energy malnutrition (PEM)/undernutrition and frailty are prevalent, overlapping conditions impacting on functional and health outcomes of older adults, but are frequently unidentified and untreated in community settings in the United States. Using the World Health Organization criteria for effective screening programs, we reviewed validity, reliability, and feasibility of data-driven screening tools for identifying PEM and frailty risk among community-dwelling older adults. The SCREEN II is recommended for PEM screening and the FRAIL scale is recommended as the most promising frailty screening tool, based on test characteristics, cost, and ease of use, but more research on both tools is needed, particularly on predictive validity of favorable outcomes after nutritional/physical activity interventions. The Malnutrition Screening Tool (MST) has been recommended by one expert group as a screening tool for all adults, regardless of age/care setting. However, it has not been tested in US community settings, likely yields large numbers of false positives (particularly in community settings), and its predictive validity of favorable outcomes after nutritional interventions is unknown. Community subgroups at highest priority for screening are those at increased risk due to prior illness, certain demographics and/or domiciliary characteristics, and those with BMI < 20 kg/m 2 or < 22 if > 70 years or recent unintentional weight loss > 10% (who are likely already malnourished). Community-based health professionals can better support healthy aging by increasing their awareness/use of PEM and frailty screening tools, prioritizing high-risk populations for systematic screening, following screening with more definitive diagnoses and appropriate interventions, and re-evaluating and revising screening protocols and measures as more data become available.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".