Estimating the Sugars Content of Simulated Diets that Follow <i>Eating Well with Canada's Food Guide</i>
Bibliographic record
Abstract
Canada's Food Guide (CFG) recommends choosing foods lower in fat, sugar and salt and provides guidance to choose foods lower in added sugars. Added sugar levels in CFG's dietary patterns have never been estimated. Objectives Estimate the proportion of calories coming from added and free sugars in CFG's dietary patterns; and assess these results against the World Health Organisation (WHO) guidance on sugars. Methods Sugar‐containing foods were assigned to 3 categories: a) naturally occurring sugars source b) added sugars source or, c) free sugars source. Secondly, we simulated 8000 diets (500 for each age/sex group) consistent with CFG's dietary patterns and estimated their sugars content. Specifically, the grams of total sugars of a food having only naturally‐occurring sugars were all counted as naturally occurring sugars; the grams of total sugars of foods containing a mix of naturally occurring sugars and added or free sugars were all counted as added sugars and/or as free sugars. [This conservative methodology tends to overestimate added/ free sugar content.] Results Mean percentage of energy (E) from total sugars was 21% (17‐24%). Added and free sugars estimates were respectively 3%E (1.7‐5.2%) and 7%E (4.5‐10%). Top contributors of sugars were from naturally occurring sources such as milk products, vegetables and fruits. An assessment of the nutrient distributions of the simulated diets against the Dietary Reference Intakes showed a low prevalence of nutrient inadequacy and an adequate energy level. Conclusion These results showed that the sugar content of CFG dietary patterns is in line with the WHO sugar's recommendations. Adopting CFG's guidance leads to low levels of added/free sugars in the diets while meeting nutrient and energy requirements.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".