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Record W4386828319 · doi:10.1038/s41430-023-01314-7

WHO guideline on the use of non-sugar sweeteners: a need for reconsideration

2023· review· en· W4386828319 on OpenAlexafffund
Tauseef Khan, Jennifer J. Lee, Sabrina Ayoub‐Charette, Jarvis C. Noronha, Néma McGlynn, Laura Chiavaroli, John L. Sievenpiper

Bibliographic record

VenueEuropean Journal of Clinical Nutrition · 2023
Typereview
Languageen
FieldMedicine
TopicDiet, Metabolism, and Disease
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
FundersInstitute of Nutrition, Metabolism and DiabetesOntario Ministry of Research and InnovationBanting and Best Diabetes Centre, University of TorontoInternational Nut and Dried Fruit CouncilMitacsNovo NordiskCanadian Institutes of Health ResearchDanoneAlmond Board of CaliforniaNational Honey BoardInstitute for the Advancement of Food and Nutrition SciencesLoblaw Companies LimitedInternational Sweeteners AssociationUniversity of TorontoAlberta Pulse Growers CommissionU.S. Department of AgricultureDiabetes CanadaGovernment of CanadaAmerican Beverage AssociationDairy Farmers of CanadaUnited Soybean BoardGeneral Mills
KeywordsSweetening agentsSugarFood scienceArtificial SweetenerGuidelineMedicineBiotechnologyEnvironmental healthBiology

Abstract

fetched live from OpenAlex

The World Health Organization’s (WHO) Nutrition and Food Safety Department recently released a guideline on the use of non-sugar sweeteners (NSS) [ 1 ] based upon the analysis of a WHO-commissioned systematic review and meta-analysis (SRMA) [ 2 ]. The guideline mentions that NSS use in randomized controlled trials (abbreviated as trials) showed a reduction in adiposity outcomes but in prospective cohort studies, NSS intake was associated with increased adiposity and chronic disease risk. Despite conflicting results between the study types, the WHO’s recommendation is very specific: "NSS not be used as a means of achieving weight control or reducing the risk of non-communicable diseases ( conditional recommendation )”.

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)0.005
metaresearch head score (Gemma)0.014
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.592
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.501
GPT teacher head0.488
Teacher spread0.013 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations45
Published2023
Admission routes2
Has abstractyes

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