READY TO EAT CEREAL (RTEC) BREAKFAST CONSUMPTION IMPROVES NUTRIENT INTAKE STATUS IN AFRICAN AMERICAN CHILDREN AND ADOLESCENTS.
Bibliographic record
Abstract
We examined the contribution of RTEC to the nutrient intake status of African Americans (AA) children and adolescents. We analyzed the 24‐h dietary recall data from 2371 participants aged 1–18 y from the 1999–2002 National Health and Nutrition Examination Survey. In all age groups, compared to breakfast skippers, RTEC consumption at breakfast significantly (p<0.05) improved daily intake profiles of fiber as well as 12 vitamins and minerals including vitamins C, B 1 , B 2 , B 6 and B 12 , folic acid, niacin, iron and zinc. In particular, RTEC consumers showed significantly (p<0.05) improved intake status of shortfall nutrients (fiber, calcium, magnesium and potassium), as compared to breakfast skippers. image RTEC consumers had the lowest % energy intake from fat while breakfast consumption was associated with higher daily energy intakes. Vitamins A and E intakes and % energy intake from saturated fat were comparable among the groups compared. The results suggest that regular consumption of a nutrient dense RTEC breakfast may improve intake of key nutrients in AA children.
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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 | 0.000 | 0.000 |
| 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".