Trends in trans fatty acid levels in the Canadian food supply (632.12)
Bibliographic record
Abstract
Dietary trans fatty acids (TFA) increase risk for heart disease. In 2007, Canada adopted voluntary TFA limits for food (<5% total fat, <2% total fat for fats) and recommended that unsaturated fat replace TFA, over saturated fat. Mandatory TFA labeling was implemented and a government‐led Trans Fat Monitoring Program (TFMP) measured and publicly reported TFA levels by company/brand. This study assessed changes in TFA levels in the food supply and if saturated fats are higher in foods with lower TFA. Data from Health Canada’s TFMP (n=921, 2006‐2009) and the University of Toronto food (n=5544, 2010‐2011) and restaurant (n=4272, 2010) databases were used. The proportion of foods meeting TFA limits improved from 75.4% in 2006‐2009 to 96.1% in 2010‐2011, particularly in packaged foods: croissants (25 to 100%), pies (36 to 98%), cakes (43 to 90%), and garlic spread (33 to 100%). Most TFMP restaurant categories, except for muffins (95%) had 100% of foods meeting TFA limits in 2010. Some food categories had a large proportion of foods exceeding TFA limits: dairy‐free cheeses (100%), frosting (72.0%), shortening (66.7%), coffee whiteners (66.7%), and restaurant‐prepared biscuits and scones (47.4%). Coffee whiteners, donuts, popcorn and frosting that exceeded TFA limits contributed >25% TFA to total fat. Saturated fat was higher (p<0.05) in some foods with low TFA levels: chocolate chip, chocolate covered and sandwich cookies, brownies and squares, cakes with pudding/mousse, dessert topping, and among restaurant‐prepared cookies. There is an impressive improvement in TFA levels in the Canadian food supply, demonstrating that voluntary limits and monitoring are effective in reducing TFA in the food supply. Efforts should focus on reducing TFA where levels remain high and on limiting the use of saturated fat to replace TFA.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.014 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".