P3-40-05 - Trends in the use of gluten-free claims on Canadian food labels between 2013-2017 and assessment of their nutritional quality
Notice bibliographique
Résumé
Gluten-free claims (GFC) on food labels are becoming popular despite the fact that 1% and 6% of the population have celiac disorder or gluten sensitivity, respectively. A previous Canadian study found that GFC were displayed on 8% of food labels; however, certain food categories (e.g., snacks, meat products) were among the ones carrying most GFC. This study aimed to assess trends in the use of GFC on Canadian food labels in top food categories carrying GFC from 2013 to 2017 and to determine the nutritional quality between products with and without GFC.This study was a repeated cross-sectional analysis of the University of Toronto Food Label Information Program 2013-2017. GFCwere identified by systematically reviewing the photographs of food labels (n=9,179) in seven food categories previously identified as carrying larger proportions ofGFC: 1) desserts; 2) desserts toppings and fillings; 3) meat products; 4) nuts and seeds; 5) sauces and dips; 6) snacks; and 7) soups.GFCwerecoded as present, if a gluten-free declaration or symbol was made on package, otherwise products were coded as claim absent. Proportions of products displaying GFCwere calculated overall and by food category.Mean contents of saturated fat (g per 100g/ml), sodium (mg per 100g/ml) and sugar (g per 100g/ml) were calculated for products with and without GFC. Chi-square and Mann-Whitney-Wilcoxon tested differences in proportions and mean contents of those nutrients between years.Results showed that among these categories, GFC have significantly increased from 11% in 2013 to 23% in 2017 (p<0.001). At a category level, snacks had the greatest increase of GFC as their prevalence doubled (15% to 33%, p<0.001), followed bynuts and seeds(12% to 27%, p<0.001) and meat products (15% to 28%, p<0.001), respectively for 2013 and 2017. The proportion of GFC in dessert toppings and fillings remained constant (16% in 2013 and 14% in 2017, p=0.74). When the nutritional composition was examined, results were mixed: in dessert toppings and filling, meat products and,nuts and seeds, products with GFC had higher contents of saturated fat, sodium and sugar in comparison to their counterpart without claims, whereas the opposite was true for foods within desserts, sauces and dips, snacks, and soups categories (p<0.001 for all nutrients, except for saturated fat innuts and seeds).In conclusion, prevalence of GFChas doubled in the Canadian food supply; such claims are not indicative of better nutritional composition.Conflict of interest:Prior coming to the University of Toronto, Beatriz Franco-Arellano was a PepsiCo Mexico employee. The company had no connection or funding to the research. Mary Lu2019Abbu00e9 declares that she has received research grants from the Canadian Institutes of Health Research, Canadian Stroke Network, Burroughs Wellcome Fund, Heart and Stroke Foundation of Canada, International Development Research Centre, University of Toronto.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,003 | 0,006 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,017 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».