Mexican children consuming breakfast and ready‐to‐eat cereals had a higher intake and adequacy of micronutrients than breakfast skippers: ENSANUT 2012 (130.4)
Bibliographic record
Abstract
Mexican children consuming breakfast and ready‐to‐eat cereals had a higher intake and adequacy of micronutrients than breakfast skippers: ENSANUT 2012 Salvador Villalpando 1 , Vanessa De la Cruz Gongora 1 , Alejandra Contreras Manzano 1 , Filiberto Beltran‐Velazquez 2 , Deisy Hervert‐Hernandez 2 1 Centro de Investigacion en Nutricion y Salud, Instituto Nacional de Salud Publica, Cuernavaca, México; 2 Kellogg Company Mexico, Queretaro, Mexico Studies in children suggest that ready to cereal (RTEC) breakfast eaters are more likely to meet daily nutrient intake guidelines and less likely to be obese. Objective: To examine the nutrient intakes of Mexican children consuming RTEC breakfast, other type of breakfast or breakfast skippers . Methods: Dietary data from children aged 1‐11 years (n=4863) participating in the Mexican National Health and Nutrition Examination Survey (ENSANUT) 2012 were analyzed. Results: Overall, 90% and 86% of children aged 1‐4 and 5‐11 years of age, respectively, ate breakfast. For 1‐4 year old children, energy intakes did not differ among breakfast consumers and breakfast skippers. Children 5‐11 y breakfast consumers, had a higher intake of energy than breakfast skippers (p<0.05). Children who ate breakfast had a higher intake of vitamins A, B2, D, calcium and zinc (p<0.05) than breakfast skippers. Particularly, children who had a breakfast with RTEC had a higher intake of vitamins A, E, B1, B2, B3, B6, folate, iron, calcium and zinc (p<0.05) than children skipping breakfast or consuming other types of breakfast. Conclusion : Children eating breakfast and especially RTEC breakfast had a higher dietary micronutrients intake than breakfast skippers. Grant Funding Source : Supported by a non‐commited grant of Kellogg Company
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".