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Consumption of 100% Fruit Juice is Associated with Better Nutrient Intake and Diet Quality but not with Weight Status in Children: NHANES 2007-2010

2015· article· en· W2102016198 on OpenAlexaffvenue
Theresa A. Nicklas, Carol E. O’Neil, Victor L. Fulgoni

Bibliographic record

VenueInternational Journal of Child Health and Nutrition · 2015
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsImpact
FundersAgricultural Research ServiceU.S. Department of Agriculture
KeywordsMedicineNutrientEnvironmental healthConsumption (sociology)Food science

Abstract

fetched live from OpenAlex

Objective: This study examined the impact of various levels of 100% fruit juice (FJ) consumption on intake of nutrients, diet quality, and weight in children using the more recent national data. Methods: This was a cross-sectional study examining the data from children 2-18 years of age (n=6,090). Intake of nutrients and diet quality were assessed using the 24-hr dietary recall and Healthy Eating Index-2010, respectively. Various consumption levels of 100% FJ were determined. Covariate adjusted linear regression means, and standard errors were determined (p<0.01). Results: Average per capita consumption of 100% FJ consumed was 3.6 fl oz (50 kilocalories; 2.9% energy intake); 30% of children 2-6 years exceeded the recommendation for 100% FJ. Among 100% FJ consumers, the mean amount of 100% FJ consumed was 10.6 fl oz (147 kilocalories; 8.4% energy intake). Intakes of vitamin C, magnesium, and potassium and overall diet quality were higher with more 100% FJ consumed; no difference was found in total fiber intake. No trends were seen in weight with increased amounts of 100% FJ consumed. Conclusions: Consumption of 100% FJ should be recommended as a component of a healthy diet.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.412

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.026
GPT teacher head0.317
Teacher spread0.291 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations23
Published2015
Admission routes2
Has abstractyes

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