Malnutrition and related risk factors in older adults from different health-care settings: an <i>enable</i> study
Bibliographic record
Abstract
OBJECTIVE: The origin of malnutrition in older age is multifactorial and risk factors may vary according to health and living situation. The present study aimed to identify setting-specific risk profiles of malnutrition in older adults and to investigate the association of the number of individual risk factors with malnutrition. DESIGN: Data of four cross-sectional studies were harmonized and uniformly analysed. Malnutrition was defined as BMI < 20 kg/m2 and/or weight loss of >3 kg in the previous 3-6 months. Associations between factors of six domains (demographics, health, mental function, physical function, dietary intake-related problems, dietary behaviour), the number of individual risk factors and malnutrition were analysed using logistic regression. SETTING: Community (CD), geriatric day hospital (GDH), home care (HC), nursing home (NH). PARTICIPANTS: CD older adults (n 1073), GDH patients (n 180), HC receivers (n 335) and NH residents (n 197), all ≥65 years. RESULTS: Malnutrition prevalence was lower in CD (11 %) than in the other settings (16-19 %). In the CD sample, poor appetite, difficulties with eating, respiratory and gastrointestinal diseases were associated with malnutrition; in GDH patients, poor appetite and respiratory diseases; in HC receivers, younger age, poor appetite and nausea; and in NH residents, older age and mobility limitations. In all settings the likelihood of malnutrition increased with the number of potential individual risk factors. CONCLUSIONS: The study indicates a varying relevance of certain risk factors of malnutrition in different settings. However, the relationship of the number of individual risk factors with malnutrition in all settings implies comprehensive approaches to identify persons at risk of malnutrition early.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".