Comparison of requirements for using health claims on foods in the European Union, the USA, Canada, and Australia/New Zealand
Bibliographic record
Abstract
Nutrition is recognized as one of the leading factors influencing the growing incidence of noncommunicable diseases. Despite society experiencing a global rise in obesity, specific populations remain at risk of nutrient deficiencies. The food industry can use health claims to inform consumers about the health benefits of foods through labeling and the broader promotion of specific food products. As health claims are carefully regulated in many countries, their use is limited due to considerable investments required to fulfill the regulatory requirement. Although health claims represent a driving force for innovation in the food industry, the risk of misleading of consumers need to be avoided. The health claim scientific substantiation process must be efficient and transparent in order to meet the needs of companies in the global market, but should be based on strong scientific evidence and plausible mechanisms of actions, to ensure highest level of consumer protection. The objective of this review is to compare the possibilities for using health claims on foods in the European Union, the USA, Canada, and Australia and New Zealand. In particular, we focused on differences in the classification of claims, on the scientific substantiation processes and requirements for health claims use on foods in the selected regions. Reduction of disease risk (RDR) claims are associated with relatively similar procedures and conditions for use, whereas several notable differences were identified for other types of claims. In all cases, RDR claims must be approved prior their introduction to the market, and only a few such claims have been authorized. Much greater differences were observed concerning other types of claims.
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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 | 0.027 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".