Micronutrient Intakes Vary by Age Group and Ethnicity in Older US Populations
Bibliographic record
Abstract
Older adults are at greater risk of malnutrition. Causes include reduced food intake, poorer taste and smell, difficulties chewing or preparing food, nutrient malabsorption in the aging digestive tract, and co‐morbidities. In addition, ethnicity may confound nutrient status. Using NHANES, 2003–2008, usual intakes of vitamins, minerals and carotenoids and percentage not meeting the EAR, where available, were determined using the National Cancer Institute method. Statistically significant differences between age groups (51–70, 71+ yrs) were calculated using a Student's t‐test and by linear regression modeling (significance was p <0.05). Defining at risk nutrients as more than 25% of population not meeting the EAR, adults 51+ yrs had lower intakes of at risk nutrients: calcium choline, lycopene, magnesium and vitamin E, and higher intakes of alpha‐ and beta‐carotene, vitamin A, C and D than adults aged 19–50 yrs. Significant differences were found comparing ages 51–70 and 71+ yrs, with lower intakes generally found in the older age group. Ethnicity significantly affected adults’ nutrient intakes for most nutrients with African Americans having the lowest intakes of all nutrients except lutein+zeaxanthin, vitamins A, C and K. In summary, nutrient intakes vary by age group and ethnicity. Adults 71+ yrs and African American seniors are at higher nutritional risk.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".