Targeting Overconsumption of Sugar-Sweetened Beverages vs. Overall Poor Diet Quality for Cardiometabolic Diseases Risk Prevention: Place Your Bets!
Bibliographic record
Abstract
Chronic overconsumption of sugar-sweetened beverages (SSBs) is amongst the dietary factors most consistently found to be associated with obesity, type 2 diabetes (T2D) and cardiovascular disease (CVD) risk in large epidemiological studies. Intervention studies have shown that SSB overconsumption increases intra-abdominal obesity and ectopic lipid deposition in the liver, and also exacerbates cardiometabolic risk. Similar to the prevalence of obesity and T2D, national surveys of food consumption have shown that chronic overconsumption of SSBs is skyrocketing in many parts of the world, yet with marked heterogeneity across countries. SSB overconsumption is also particularly worrisome among children and adolescents. Although the relationships between SSB overconsumption and obesity, T2D, and CVD are rather consistent in epidemiological studies, it has also been shown that SSB overconsumption is part of an overall poor dietary pattern and is particularly prevalent among subgroups of the population with low socioeconomic status, thereby questioning the major focus on SSBs to target/prevent cardiometabolic diseases. Public health initiatives aimed specifically at decreasing SSB overconsumption will most likely be successful in influencing SSB consumption per se. However, comprehensive strategies targeting poor dietary patterns and aiming at improving global dietary quality are likely to have much more impact in addressing the unprecedented public health challenges that we are currently facing.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".