Canadian Dietary Intakes Assessed by Nutrient Profiling Models and Association with Mortality and Cardiovascular Disease
Bibliographic record
Abstract
Purpose: Nutrient profiling (NP) ranks foods according to nutritional composition and underpins policies (e.g., front-of-package (FOP) labelling). This study aimed to evaluate Canadian adults’ dietary intakes using proposed Canadian FOP “high-in” labelling thresholds and international NP models (i.e., Ofcom, FSANZ, and Nutri-Score) and examine the association between intakes using international NP models and all-cause mortality and cardiovascular disease (CVD). Methods: Intakes from the Canadian Community Health Survey-Nutrition (CCHS-Nutrition) 2004 and 2015 were given NP scores and assessed against FOP thresholds. CCHS-Nutrition 2004 was linked with death records (Canadian Vital Statistics Database, n = 6767) and CVD incidence and mortality (hospital Discharge Abstract Database, n = 6420) until December 2017. Results: Foods that would require FOP labels, should there be such regulation in Canada, contributed 38% of calories. Association between NP scores and mortality was significant for Ofcom, FSANZ, and Nutri-Score (hazard ratio (HR) in highest quintile (lowest quality): 1.73, 95%CI [1.20–2.49], 1.59[1.15–2.21], and 1.75[1.18–2.59], respectively), and for CVD incidence, among males (HR in highest quintile: 2.11[1.15–3.89], 1.74[1.07–2.84], and 2.29[1.24–4.24], respectively). Conclusions: Canadians had moderately healthy intakes. NP systems could discriminate between low and high dietary quality such that adults with the lowest diet quality were more likely to experience all-cause mortality and CVD events (for males).
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".