Bean consumption by children is associated with better nutrient intake and lower body weights and waist circumferences
Bibliographic record
Abstract
Using the latest data from the National Nutrition and Health Examination Survey, 1999‐2002, the present study examined the association of bean consumption with food/nutrient intake and certain health parameters in children of differing ages (4–11 and 12–19 yr; n=2740, N=4392, respectively). Comparisons of physiological parameters and food/nutrient intake among bean consumers and non‐consumers were made after adjustment for key covariates including gender, age, ethnicity and calorie intake. Body mass index analyses were conducted using CDC growth chart programs to adjust BMI for age. Bean consuming young children reported greater (p< 0.01) intake of protein (67.3 ± 1.6 vs. 65.2 ± 0.2 g/d), dietary fiber (18.2 ± 0.9 vs. 11.7 ± 0.2 g/d), potassium (2445 ± 70 vs. 2141 ± 28 mg/d) and magnesium (253 ± 7 vs. 214 ± 3 mg/d) relative to non‐bean consumers. Similar differences were seen in older children. There were no effects of bean consumption on body weight, BMI, and waist size in young children. However, in children 12–19 yrs, bean consumption was associated with a lower (p<0.05) body weight (61.1± 1.3 vs. 64.4 ± 0.4 kg) and waist circumference (78 ± 0.94 vs. 80.4 ± 0.36 cm) compared to non‐consumers. In children 4–19 yrs, bean consumers trended towards a reduced risk of being overweight (OR: 0.68; 95%CI: 0.44‐1.05, p=0.08). In conclusion, the present study suggests that bean consumption in all children is associated with better nutrient intake, while in older children bean consumption is also associated with lower body weights and smaller waist sizes. These findings suggest a role for bean consumption in improving nutrient density of the diet and may help in reducing overweight and obesity in children. Supported by Bush Brothers & 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.002 | 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".