MétaCan
Menu
Back to cohort
Record W1593385164 · doi:10.1002/oby.20845

Association of sports drinks with weight gain among adolescents and young adults

2014· article· en· W1593385164 on OpenAlexaff
Alison E. Field, Kendrin R. Sonneville, Jennifer Falbe, Alan Flint, Jess Haines, Bernard Rosner, Carlos A. Camargo

Bibliographic record

VenueObesity · 2014
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsUniversity of Guelph
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesInstitut National de la Santé et de la Recherche Médicale
KeywordsMedicineBody mass indexConfidence intervalObesityDemographyScreen timeProspective cohort studyYoung adultEnvironmental healthGerontologySurgeryInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: Sales of regular soda were declining, but sales of other sweetened beverages, such as sports drinks, were increasing. Our objective was to determine the prospective associations between sports drinks and body mass index (BMI) gains among adolescents and young adults. METHODS: 4121 females and 3438 males in the Growing Up Today Study II, aged 9-16 in 2004, from across the United States were followed prospectively. Data were collected by questionnaire in 2004, 2006, 2008, and 2011. Servings per day of various beverages were assessed with a food frequency questionnaire. RESULTS: Among the girls, each serving per day of sports drink predicted an increase of 0.3 BMI units (95% confidence interval (CI) CI 0.03-0.54) more than their peers over the next 2-3 years. Among the males, each serving of sports drinks predicted a 0.33 BMI (95% CI 0.09, 0.66) increase. In addition, boys who increased their intake over the 2-3 year interval gained significantly more than their peers during the same time interval. CONCLUSIONS: Intake of sports drinks predicted larger increases in BMI among both females and males. Our results suggest that school policies focused on obesity prevention should be augmented to restrict sports drinks.

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 score0.003
Threshold uncertainty score0.442

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 head0.003
GPT teacher head0.203
Teacher spread0.199 · 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

Citations52
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueObesitySame topicObesity, Physical Activity, DietFrench-language works237,207