Association of raisin and raisin‐containing food consumption with nutrient intake and diet quality in <scp>US</scp> children: <scp>NHANES</scp> 2001‐2012
Bibliographic record
Abstract
Abstract Background Raisins are a commonly consumed dried fruit and given their nutrient profile may offer nutritional and health benefits. Objective To examine the association between consumption of raisins and raisin‐containing foods with nutrient intake and dietary quality in children. Methods National Health and Nutrition Examination Survey (NHANES) data for 2001–2012 in those 2–18 years of age (n = 20,175) were used. Consumers of raisins (n = 154, 51.6% female) and raisin‐containing foods (n = 1,993, 52.5% female) were defined as reporting any consumption of raisins and raisin‐containing foods, respectively, during the first 24‐hr diet recall. Diet quality was assessed using the Healthy Eating Index (HEI)‐2010. Regression analyses were conducted comparing consumers and nonconsumers using appropriate sample weights and adjusted for demographic and lifestyle covariates with significance set at p < 0.01. Results Regarding “nutrients of public health concern/shortfall nutrients” and “nutrients to limit,” raisin consumers had higher intakes of dietary fiber (23%), potassium (16%), magnesium (12%) with lower intakes of added sugars (−19%) than nonconsumers. Similarly, consumers of raisin‐containing foods also had higher intakes of dietary fiber (15%), potassium (5%), magnesium (11%), iron (6%), vitamin A (10%), and vitamin E (13%) and lower intake of sodium (−5%). Consumers of raisin and raisin‐containing foods had higher intakes of fruits (60%, 16%, respectively), whole fruits (119%, 23%, respectively) and whole grains (44%, 93%, respectively) and had a better diet quality as per higher total HEI 2010 scores (22%, 8%, respectively) than nonconsumers. Conclusion In conclusion, consumption of raisins or raisin‐containing foods was associated with better nutrient intake and diet quality in American 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 | 0.001 | 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".