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Record W4280617295 · doi:10.3390/nu14102091

Non-Nutritive Sweetener Intake Is Low in Preschool-Aged Children in the Guelph Family Health Pilot Study

2022· article· en· W4280617295 on OpenAlexafffundabout
Anisha Mahajan, Jess Haines, Alex Carriero, Jaimie L. Hogan, Jessica Yu, Andrea C. Buchholz, Alison M. Duncan, Gerarda Darlington, David W.L.

Bibliographic record

VenueNutrients · 2022
Typearticle
Languageen
FieldNursing
TopicBiochemical Analysis and Sensing Techniques
Canadian institutionsUniversity of Guelph
FundersCanadian Institutes of Health ResearchHeart and Stroke Foundation of Canada
KeywordsSucraloseAspartameMedicineArtificial SweetenerEnvironmental healthLimitingFood intakeAdded sugarFruit juicePopulationFood scienceSugarPediatricsBiology

Abstract

fetched live from OpenAlex

There is limited research on the intake of non-nutritive sweeteners (NNS) among preschool-aged children. Canada’s Food Guide suggests limiting intake of NNS for all population groups and Health Canada recommends that young children (<2 years) avoid consuming beverages containing NNS. The aim of this study was to investigate the frequency and type of non-nutritive sweetener (NNS) intake in preschool-aged children participating in the Guelph Family Health Study pilots. Parents (n = 78 families) completed 3-day food records (n = 112 children; n = 55 females, n = 57 males; 3.6 years ± 1.3). Nineteen children (17%) reported consumption of foods or beverages containing NNS. Food sources with NNS included: freezies, oral nutritional supplements, flavored water, carbonated drinks, sugar free jam and protein powder. The majority of NNS contained in these foods were identified as stevia leaf extract, acesulfame K, sucralose, monk fruit extract and aspartame. Future research should continue to study NNS intake patterns longitudinally in children and examine the association of NNS intake with diet quality and health outcomes.

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)0.001
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 score0.085
Threshold uncertainty score0.744

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.023
GPT teacher head0.281
Teacher spread0.259 · 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

Citations5
Published2022
Admission routes3
Has abstractyes

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