Abstract P3107: Relationship between adherence to Canada’s Food Guide 2019 and the risk of cardiovascular disease in Canada: A Protocol
Bibliographic record
Abstract
Introduction: Cardiovascular disease (CVD) is a leading cause of death in Canada. A healthy diet plays an integral role in CVD prevention. In 2019, Canada’s Food Guide (CFG) was updated to reflect current evidence on healthy eating. The Healthy Eating Food Index (HEFI) was developed as a tool to measure adherence to CFG. However, the extent to which adherence to CFG influences CVD remains unexplored within Canada. Objective: 1.) Assess the association between HEFI scores and the risk of CVD in a population-based cohort. 2.) Estimate the number of preventable CVD-related deaths from improvements in HEFI scores. 3.) Estimate potential healthcare cost savings from improvements in HEFI scores. Methods: Data linkages between the Canadian Community Health Survey – Nutrition (2004) and the Canadian Vital Statistics Death and Discharge Abstract Database (2017) will be used, with over 80% of CCHS respondents successfully linked to these databases. Weighted Cox proportional hazards models will be used to examine the prospective association between HEFI scores and CVD outcomes. Analyses will be stratified by sex to account for potential differences in associations between males and females. Multivariate models will be adjusted for dietary recall-related factors (e.g. weekday/weekend, sequence of recall), sociodemographic factors (e.g., age, education) and lifestyle-related factors (e.g., alcohol consumption, physical activity, smoking). The population attributable fraction (PAF) will be estimated for the number of preventable CVD deaths attributable to improvements in HEFI scores. To estimate the potential economic impact of HEFI improvements, the PAF will be multiplied by direct and indirect costs of CVD. Significance: This study will be the first to explore the prospective association between HEFI scores and CVD risk in a Canadian population as well as to quantify the number of preventable deaths and potential economic savings associated with improvements in HEFI scores. Analyses are currently underway, and results are forthcoming. Findings will inform dietary guidance and shape Canadian health and economic policy strategies, enhancing both public health and financial outcomes.
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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 | 0.016 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.083 | 0.012 |
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".