Bean consumption by adults is associated with a more nutrient dense diet and a reduced risk of obesity
Bibliographic record
Abstract
A bean‐rich diet improves satiety, facilitates nutrient intake and has been associated with a significant reduction in risk of obesity. Using the latest data from the National Nutrition and Health Examination Survey (1999–2002), the present study examined the association of bean consumption with nutrient intake and certain health parameters. Comparisons of physiological parameters and food/nutrient intake among bean consumers and non‐consumers were made after adjustment for several covariates including age, gender, ethnicity, and calorie intake. In adults 20+ years (N=8,374) bean consumption was associated with greater (p<0.01) intake of dietary fiber (22.8 ± 0.5 vs. 15.2 ± 0.2 g/d), potassium (3139 ± 57 vs. 2724 ± 23 mg/d) and magnesium (335 ± 4 vs. 280 ± 3 mg/d) and with a decreased intake of discretionary fat (60 ± 1 vs. 63.2 ± 0.4 g/d), and added sugars (19.4 ± 0.9 vs. 22.2 ± 0.5 tsp/d). Bean consumption was also associated with lower (p<0.05) body weight (77.5 ± 1.1 vs. 80.5 ± 0.3 kg) and a reduced waist circumference (94.2 ± 1.0 vs. 96.1 ± 0.3 cm) relative to non‐bean consumption. Bean consumers had a reduced risk of increased waist size (OR=0.77; 95% CI: 0.62, 0.95) and a reduced risk of being obese (OR=0.78; 95% CI: 0.64, 0.97) relative to non‐users of beans. Overall, bean consumption is associated with a more nutrient dense diet and with a more desirable body weight. 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 | 0.000 |
| Bibliometrics | 0.000 | 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".