Characteristics of Nutritionally Vulnerable Community-Dwelling Older Adults Consuming Oral Nutritional Supplements (OR16-08-19)
Bibliographic record
Abstract
There are limited data on nutrition risk profiles of community-dwelling older adults and their consumption patterns of oral nutritional supplements (ONS). This study assessed the characteristics of nutritionally vulnerable ONS users versus non-ONS users. A cross-sectional study of White, Black and Hispanic community-dwelling adults, ≥55 years of age, residing in Chicago metropolitan area (MSA), USA was conducted over a 4-month period. Demographic characteristics, Katz’s activities of daily living (ADL), healthcare resource utilization, and ONS consumption in the last six months were collected via a telephone survey. Nutrition risk was assessed via the abridged Patient Generated Subjective Global Assessment (abPG-SGA) and the DETERMINE checklist. Characteristics of ONS and non-ONS users were compared using t-tests and chi-square tests. A multivariable logistic regression assessed which characteristics were independently associated with ONS use. Of 1001 participants surveyed, 996 provided data on ONS use; 26% (n = 261) were classified as ‘high’ nutrition risk, and 11% (n = 114) reported consuming ONS. ONS users reported consuming on average 4.7 bottles/week, with recommendation primarily received from their physician (37%). ONS users were more likely to be at high nutritional risk than non-users per both abPG-SGA (43% vs 24%, P < .001) and DETERMINE (68% vs 48%, P < .001) results. They were less likely to be independent on all ADLs (86% vs 92%, P = .03), reported higher healthcare utilization, and were more likely to take ≥3 drugs/day (77% vs 53%, P < .001). Higher nutritional risk (per abPG-SGA), lower body mass index, hospitalization in past 6 months, and taking ≥3 drugs/day were significantly (P < .05) associated with ONS use. Although one in four older community-dwellers living in the Chicago MSA were at high nutrition risk, only 11% of them reported consuming ONS. Older adults are more likely to consume ONS and experience poor health outcomes and polypharmacy. These results suggest that identification of nutrition risk among community-dwelling older adults remains unaddressed and may not result in nutrition interventions. Improved identification of nutrition risk and implementation of targeted interventions with ONS is warranted for older community-dwelling adults. Abbott, Columbus, OH, USA.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".