High prevalence of malnutrition among elderly veterans in home based primary care
Bibliographic record
Abstract
INTRODUCTION: Elderly Veterans enrolled in VA Home Based Primary Care (HBPC) programs suffer from many diseases including malnutrition. Nutrition screening tools exist in the VA system but they are inconsistently utilized across ambulatory care programs and are neither research validated nor comparable with non-VA populations. The Mini-Nutritional Assessment short-form (MNA-SF) has been validated in international studies in a variety of settings. The primary aim of this study was to find the prevalence of malnutrition among Veterans enrolled in HBPC programs. The secondary objective was to determine the feasibility of adopting a validated nutrition screening tool (Mini-Nutritional Assessment short-form (MNA-SF)). METHODS: 2252 veterans age 65 and older from 18 HBPC programs from across the country participated in the study. The study period was between April and September 2012. WinPepi (version 11.25) was used for descriptive analysis. RESULTS: We found that the prevalence of malnutrition was 15% (344/2252) and the prevalence of at risk for malnutrition was 40.3% (909/2252). DISCUSSION: The MNA-SF is an efficient nutrition screening tool and it can be successfully used for the elderly veterans. The prevalence of malnutrition among veterans was high compared to the community dwelling U.S. civilian elderly population. By preventing and treating malnutrition, health care systems should be able to reduce overall health care costs.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".