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Record W3110297852 · doi:10.1093/nutrit/nuaa123

Low-calorie sweeteners and human health: a rapid review of systematic reviews

2020· review· en· W3110297852 on OpenAlexaff
Lesley Andrade, Kirsten Lee, Allison C. Sylvetsky, Sharon I. Kirkpatrick

Bibliographic record

VenueNutrition Reviews · 2020
Typereview
Languageen
FieldNursing
TopicBiochemical Analysis and Sensing Techniques
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMedicineSystematic reviewCochrane LibraryCalorieEnvironmental healthMEDLINEInsulin resistanceRandomized controlled trialIntensive care medicineDiabetes mellitusInternal medicineEndocrinology

Abstract

fetched live from OpenAlex

INTRODUCTION: Low-calorie sweeteners are increasingly prevalent in the food supply and their consumption has increased in recent decades. Although low-calorie sweeteners approved for use are considered safe from a toxicological perspective, their short- and long-term impacts on chronic disease risk remain uncertain. The aim of this review was to summarize the evidence from systematic reviews on low-calorie sweetener use and chronic conditions and risk factors in children and adults. METHODS: MEDLINE and the Cochrane Database of Systematic Reviews were searched to identify systematic reviews of randomized and nonrandomized studies that considered low-calorie sweeteners in relation to type 2 diabetes, cardiovascular disease, cancer, anthropometric measures, hypertension, hyperglycemia, hyperlipidemia, insulin resistance, and dental caries. Data were extracted from 9 reviews deemed of moderate or high quality on the basis of AMSTAR-2. RESULTS: Narrative synthesis suggested inconsistent evidence on low-calorie sweetener use in relation to chronic conditions and associated risk factors, with nonrandomized studies suggesting positive associations and randomized studies suggesting negative or no associations. CONCLUSION: Continued research on the long-term health impacts of low-calorie sweeteners across all life stages is warranted.

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)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Meta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.427
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0160.003
Bibliometrics0.0000.001
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 head0.166
GPT teacher head0.401
Teacher spread0.234 · 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 designSystematic review
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

Citations26
Published2020
Admission routes1
Has abstractyes

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