A narrative review of the effects of sugar-sweetened beverages on human health: A key global health issue
Bibliographic record
Abstract
The provision of healthy and safe food is vital for human health, and the addition of unnecessary sugars in foodstuffs is an important global issue, leading to multiple long- and short-term health issues and spiraling costs for individuals and governments alike. The negative effect of excess sugar consumption contributes to adverse health conditions, including obesity, type 2 diabetes, and poor oral health in both high and low resource settings. A key plank of governmental and health promotion bodies' nutritional guidance is to raise public awareness of "hidden" sugars, salt, and fats, such as found in processed foods and sugar-sweetened beverages (SSBs), and guide individuals to reduce their consumption. This rapid narrative review brings together some of the key issues identified in the literature around the consumption of SSBs, including patterns of consumption, the general impact on human health and nutrition, specific effects on oral health and the oral microbiome, and strategies to address over-consumption. The range of long-term adverse effects on health is often misunderstood or unknown by the public. However, some strategies have succeeded in reducing the consumption of SSBs, including public health strategies and interventions and the imposition of taxes or levies, and this article makes recommendations for action.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".