11. Sugar and health: a food-based dietary guideline for South Africa
Bibliographic record
Abstract
The intake of added sugar appears to be increasing steadily across the South African population. Children typically consume approximately 40-60 g/day, possibly rising to as much as 100 g/day in adolescents. This represents roughly 5-10% of dietary energy, but could be as much as 20% in many individuals. This paper briefly reviews current knowledge on the relationship between sugar intake and health. There is strong evidence that sugar makes a major contribution to the development of dental caries. The intake of sugar displaces foods that are rich in micronutrients. Therefore, diets that are rich in sugar may be poorer in micronutrients. Over the past decade, a considerable body of solid evidence has appeared, particularly from large prospective studies, that strongly indicates that dietary sugar increases the risk of the development of obesity and type 2 diabetes, and probably cardiovascular disease too. These findings point to an especially strong causal relationship for the consumption of sugar-sweetened beverages (SSBs). We propose that an intake of added sugar of 10% of dietary energy is an acceptable upper limit. However, an intake of < 6% energy is preferable, especially in those at risk of the harmful effects of sugar, e.g. people who are overweight, have prediabetes, or who do not habitually consume fluoride (from drinking fluoridated water or using fluoridated toothpaste). This translates to a maximum intake of one serving (approximately 355 ml) of SSBs per day, if no other foods with added sugar are eaten. Beverages with added sugar should not be given to infants or to young children, especially in a feeding bottle. The current food-based dietary guideline is: “Use foods and drinks containing sugar sparingly, and not between meals”. This should remain unchanged. An excessive intake of sugar should be seen as a public health challenge that requires many approaches to be managed, including new policies and appropriate dietary advice.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 0.008 |
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".