Consumption of 100% juices is not associated with being overweight or risk for being overweight in children
Bibliographic record
Abstract
Using four‐year National Health and Nutrition Examination Survey (NHANES) data (1999–2002), we examined the impact of consumption of 100% fruit and other juices by children ages 2–18 yrs on body weight, being overweight, and risk of being overweight. We separated the data first into two groups, young children (2–11 yrs) and older children (12–18 yrs), and then into 100% juice consumers (those who reported any amount of 100% juice consumption in the 24‐hr recall) and non‐consumers. Body weight, body mass index (BMI), and waist size were compared, with the analyses adjusted for gender, ethnicity, age and calories. We used the CDC growth chart data to establish percentiles of weight for each age group and then conducted logistic regression to ascertain if the odds ratio of being overweight (BMI ≥ 95 th percentile) or the risk of being overweight (BMI ≥ 85 th percentile) was different in 100% juice drinkers compared to non‐drinkers. In children 2–18 yrs we found no significant differences in body weight, body mass index, and waist size between juice consumers and non‐consumers. In children 12–18 yrs, BMI was significantly lower (p< 0.05) in juice consumers vs non‐consumers (22.4 ± 0.2 vs 23.1 ± 0.2, kg/m 2 ). For children 2–18 yrs, there were no differences in percentile weight for age, Z‐score for weight for age, or percentile BMI for age. However, juice consumers had a significantly lower (p< 0.05) Z‐score for BMI for age. There were no differences based on juice use for odds ratio for being overweight but juice consumers had an 18% lower (95% CI: 0.69, 0.96) risk for being overweight. (Supported by the Juice Products Association.)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".