EFFECTS OF NUTRIENTS AND BODY MEASUREMENTS ON MORTALITY RISK IN FRAIL PEOPLE
Bibliographic record
Abstract
Emerging evidence suggests that appropriate nutritional care can mitigate frailty and its complications. We investigated the association between nutrition and frailty across the life course, including the effect of nutrition-related parameters on mortality in frail people. Participants aged ≥20 years with frailty and nutrition data (n=8,482) from the 2003–2006 cohorts of the National Health and Nutrition Examination Survey were included. Four anthropometric parameters and 33 nutrients with established cut points obtained by 24-hour dietary recall were studied. A frailty index (FI) was constructed from 36 items, excluding items related to nutrition; 1,160 people (13.7%) were classified as frail (FI>0.25). Mortality data were obtained from death certificate records until 2011. We performed Cox regression analysis adjusting for age, sex, and energy intake. The percentage of individuals with abnormal nutritional parameters significantly increased with higher frailty for 22 nutrients (e.g. energy, protein, tocopherol, folate, cobalamin, essential fatty acid, omega-3 intake) and all anthropometric measures. In frail people, low body mass index (HR 4.56, 95%CI 2.29–9.06), tocopherol (2.40, 1.07–5.40), skin fold (1.65, 1.20–2.26), and energy consumption (1.59, 1.21–2.09) and weight loss (1.44, 1.09–1.91) were significantly associated with higher mortality risk. Low fat intake (0.61, 0.37–0.99) and being overweight (0.73, 0.57–0.95) were associated with lower mortality risk. Most nutritional parameters changed with frailty but not all increased the mortality risk in frail people. Being underweight and having low tocopherol intake was highly associated with increased mortality risk whereas being overweight and having low fat intake was associated with decreased mortality risk in frail people.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".