Low-calorie sweeteners and human health: a rapid review of systematic reviews
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".