Canadian Nutrition Society: 2021 Scientific Abstracts: Canadian Nutrition Society Annual Conference
Bibliographic record
Abstract
An unhealthy diet is a critical modifiable risk factor for chronic diseases.The Diabetes Canada Clinical Practice (DCCP) Guidelines encourage healthy eating by recommending consumption of certain foods or nutrients (e.g., fruits and vegetables, fibre), while limiting the intake of others (e.g., high salt/sugar foods).However, many consumers find it challenging to interpret these recommendations.Nutrient profiling (NP) models are interpretative tools that set nutrient thresholds aligned with dietary guidelines, which can be used to guide consumers towards healthier food choices.To develop a NP model based on the healthy eating recommendations in the DCCP guidelines.A systematic methodology to assess foods in alignment with DCCP guidelines was developed, using the University of Toronto Food Label Information Program database 2017 (n=17,360 packaged foods and beverages).The DCCP-NP model categorizes individual food and beverage items into two categories (i.e., 'in alignment' and 'not in alignment') with the guidelines, based on specific nutrient thresholds (i.e., high-sodium, high-fat, highsugar) or recommended food groups (e.g., whole grains).Products were categorized according to Health Canada's Table of Reference Amounts.The DCCP-NP model requires a 'pass' on all four steps to be considered 'in alignment' with this model: 1) exclude processed product (i.e., choosing whole and less refined foods); 2) lean animal protein (≤10% of total fat) and more vegetable protein; 3) low glycemic-index foods; and 4) foods without excessive saturated fats (≤9% daily value).Overall, 10% of packaged foods were 'in alignment' the DCCP-NP.Specifically, 77% of nuts/seeds, 46% of legumes, 31% of cereals, 23% of vegetables 17% of fruits, 13% of eggs, 12% of beverages, 11% of marine, 3% of dairy, 3% of potatoes, 2% of salads and 1% of processed meat were 'in alignment' with the DCCP-NP.This study developed the first NP model specific for an at-risk population.This indicates that very few packaged foods and beverages meet the standards in the DCCP guidelines, suggesting an overall low nutritional quality of the packaged food supply.People with diabetes can choose very few packaged foods and still follow the DCCP guidelines.(Sanofi Pasteur
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.443 | 0.242 |
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".