Sugars and obesity: Is it the sugars or the calories?
Bibliographic record
Abstract
Abstract Sugars containing fructose are emerging as the dominant public health targets for their role in the epidemic of overweight and obesity. This position is largely supported by ecological observations, rodent models of overfeeding and select human trials. Higher level evidence from systematic reviews and meta‐analyses of controlled dietary trials has yet to show convincingly that fructose‐containing sugars behave differently from any other forms of energy (especially refined starch and fat). Prospective cohort studies, which provide the strongest observational evidence, have shown an association between risk of overweight and obesity and fructose‐containing sugars consumed as sugar‐sweetened beverages but not as total sugars or other important sources of added sugars such as cakes, pastries and sweets. Comparative analyses show that high intakes of other highly palatable foods such as refined grains, processed meats, red meats, French fries and potato products, as well as physical inactivity may play an equal or greater role in weight gain and the risk of overweight and obesity. The contributions of these factors are also difficult to disentangle from that of sugar‐sweetened beverages owing to their collinearity with sugar‐sweetened beverages as part of a Western dietary pattern and lifestyle. Attention needs to remain focused on decreasing overconsumption of all foods associated with overweight and obesity. Sugar‐sweetened beverages and foods are certainly an important place to start but should not draw attention away from the issue of overconsumption in general.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| 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".