Assessing dietary intake among infants and toddlers 0–24 months of age in Baltimore, Maryland, USA
Bibliographic record
Abstract
OBJECTIVE: To characterize food and nutrient intake and develop a population-specific food list to be used as a comprehensive dietary assessment tool for Baltimore infants and toddlers aged 0-24 months. The data were used to inform the Growing Leaps and Bounds (GLB) program, which promotes early obesity prevention among Baltimore infants and toddlers. RESEARCH METHODS & PROCEDURES: A cross-sectional dietary survey using 24-hour recalls among randomly selected primary caregivers of infants and toddlers was conducted. RESULTS: Data were collected from 84 children, (response rate 61%) 45 boys; 39 girls. Mean daily energy intakes were 677 kcal, 988 kcal, and 1,123 kcal for children 0-6 months, 7-12 months and 13-24 months, respectively. Infants 0-6 months had higher percentage of energy from fat (48%) than infants 7-12 months (34%) and 13-24 months (31%). Mean daily intakes for all nutrients among 0-12 months old were ≥ Dietary Reference Intakes (DRI), while toddlers 13-24 months had inadequate vitamins A, D, and E intake. Breastfeeding occurred in 33% of infants and toddlers 0 to 6 months, while less than 3% of those aged 7 to 24 months were breastfed. A 104-item food list with eight food and drink categories was developed. CONCLUSIONS: Infants were formula fed with a higher frequency than they were breastfed. The consumption of high-sugar and high-fat foods (e.g. sweetened drinks, French fries) increased with each age group, which can increase the risk of childhood obesity.
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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 | 0.001 | 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 | 0.001 | 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".