Towards an Evidence-Based Recommendation for a Balanced Breakfast—A Proposal from the International Breakfast Research Initiative
Bibliographic record
Abstract
The International Breakfast Research Initiative (IBRI) set out to derive nutritional recommendations for a balanced breakfast using a standardized analysis of national nutrition surveys from Canada, Denmark, France, Spain, UK and the US. In all countries, the frequency of breakfast consumption by age was high and U-shaped with children and older adults having a higher frequency of breakfast consumption. Breakfast contributed 16% to 21% of daily energy intake. In all countries, breakfast was a carbohydrate- and nutrient-rich meal, providing more carbohydrates (including sugars), thiamin, riboflavin, folate, calcium, potassium, and magnesium, and less vitamin A, fats and sodium relative to its contribution to daily energy intakes. Breakfast consumers were stratified by tertiles of the Nutrient Rich Foods (NRF) index, used as a measure of diet quality. Breakfast intakes associated with the top tertile of NRF, along with the Codex Alimentarius international food standards and World Health Organization (WHO) diet guidelines, were used to derive the proposed nutrient recommendations. The goal was to preserve the nutrient density of existing breakfasts, while addressing concerns regarding added sugars, saturated fats, dietary fiber, and vitamin D. This initiative is unique in seeking to derive nutrient recommendations for a specific meal using the observed nutritional profile of such meal.
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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 | 0.212 | 0.273 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.011 | 0.019 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.019 | 0.011 |
| Research integrity | 0.041 | 0.051 |
| Insufficient payload (model declined to judge) | 0.007 | 0.010 |
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".