Variability in the reported energy, total fat and saturated fat contents in fast-food products across ten countries
Bibliographic record
Abstract
OBJECTIVE: Fast foods are often energy dense and offered in large serving sizes. Observational data have linked the consumption of fast foods to an increased risk of obesity and related diseases. DESIGN: We surveyed the reported energy, total fat and saturated fat contents, and serving sizes, of fast-food items from five major chains across ten countries, comparing product categories as well as specific food items available in most countries. SETTING: MRC Human Nutrition Research, Cambridge, UK. SUBJECTS: Data for 2961 food and drink products were collected, with most from Canada (n 550) and fewest from the United Arab Emirates (n 106). RESULTS: There was considerable variability in energy and fat contents of fast foods across countries, reflecting both the portfolio of products and serving size variability. Differences in total energy between countries were particularly noted for chicken dishes (649-1197 kJ/100 g) and sandwiches (552-1050 kJ/100g). When comparing the same product between countries variations were consistently observed in total energy and fat contents (g/100 g); for example, extreme variation in McDonald's Chicken McNuggets with 12 g total fat/100 g in Germany compared with 21·1 g/100 g in New Zealand. CONCLUSIONS: These cross-country variations highlight the possibility for further product reformulation in many countries to reduce nutrients of concern and improve the nutritional profiles of fast-food products around the world. Standardisation of serving sizes towards the lower end of the range would also help to reduce the risk of overconsumption.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".