The burden of excessive saturated fatty acid intake attributed to ultra-processed food consumption: a study conducted with nationally representative cross-sectional studies from eight countries
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Cross-sectional nutritional survey data collected in eight countries were used to estimate saturated fatty acid intakes. Our objective was to estimate the proportion of excessive saturated fatty acid intakes (>10 % of total energy intake) that could be avoided if ultra-processed food consumption was reduced to levels observed in the first quintile of each country. Secondary analysis was performed of 24 h dietary recall or food diary/record data collected by the most recently available nationally representative cross-sectional surveys carried out in Brazil (2008-9), Chile (2010), Colombia (2005), Mexico (2012), Australia (2011-12), the UK (2008-16), Canada (2015) and the US (2015-16). Population attributable fractions estimated the impact of reducing ultra-processed food consumption on excessive saturated fatty acid intakes (above 10 % of total energy intake) in each country. Significant relative reductions in the percentage of excessive saturated fatty acid intakes would be observed in all countries if ultra-processed food consumption was reduced to levels observed in the first quintile's consumption. The reductions in excessive intakes ranged from 10⋅0 % (95 % CI 6⋅2-13⋅6 %) in Canada to 35⋅0 % (95 % CI 28⋅7-48⋅0 %) in Mexico. In all eight studied countries, all presenting more than 30 % of intakes with excessive saturated fatty acids, lowering the dietary contribution of ultra-processed foods to attainable, context-specific levels was shown to be a potentially effective way to reduce the percentage of intakes with excessive saturated fatty acids, which may play an important role in the prevention of non-communicable diseases, particularly cardiovascular diseases.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it