MétaCan
Menu
Back to cohort
Record W4405459037 · doi:10.3390/nu16244345

Defining “High-In” Saturated Fat, Sugar, and Sodium to Help Inform Front-of-Pack Labeling Efforts for Packaged Foods and Beverages in the United States

2024· article· en· W4405459037 on OpenAlexaboutno aff
Elizabeth Dunford, Donna R. Miles, Bridget Hollingsworth, S. N. HELLER, Barry M. Popkin, Shu Wen Ng, Lindsey Smith Taillie

Bibliographic record

VenueNutrients · 2024
Typearticle
Languageen
FieldMedicine
TopicConsumer Attitudes and Food Labeling
Canadian institutionsnot available
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesBloomberg Philanthropies
KeywordsFood and drug administrationSaturated fatNutrition LabelingFood productsAdded sugarNutrition facts labelSugarTrans fatEnvironmental healthBusinessMedicineFood scienceMarketingAgricultural scienceEnvironmental scienceChemistry

Abstract

fetched live from OpenAlex

Background: To help consumers make healthier choices, the US Food and Drug Administration (FDA) has been charged with developing a front-of-package label (FOPL) to appear on US packaged foods and beverages. One option being explored is the use of “high-in” FOPLs for added sugar, sodium, and saturated fat using a threshold of ≥20% of the recommended daily value (%DV) per portion/serving size to define “high-in”. While research has addressed what FOPL designs are most effective at visually communicating “high-in”, less attention has been paid to the nutrient profile model (NPM) used to decide which products should receive these labels. In addition, several established regional NPMs already exist that identify products that are high in nutrients of concern, but it is unclear how these compare to the FDA’s %DV approach. Methods: We used a dataset of 51,809 US products from Mintel’s Global New Products Database to examine how the FDA’s current definition of “high-in” compares to three established regional NPMs: the Canadian NPM, the Pan American Health Organization (PAHO) NPM, and Chile’s NPM. Results: Overall agreement between the four NPMs was 51% for foods and 72% for beverages, with highest agreement in categories such as sweetened sodas (87%), and lowest agreement in categories such as bread (14%) and salty snacks (29%). The Canadian NPM showed the highest agreement to the FDA “high-in” criteria while the Chilean and PAHO models had lower agreement. For many food categories, the FDA’s definition of “high-in” would require the fewest products to carry a “high-in” label. This issue was particularly pronounced in categories that tend to be served in small portions (e.g., salty snacks, bars), but disappeared or reversed for categories that are served in larger portions (e.g., frozen and non-frozen main dishes). Conclusions: The NPM chosen has important policy implications for an FOPL system’s ability to identify unhealthy foods and incentivize companies to reformulate products. Based on these results, the FDA should consider using a stronger NPM similar to those used elsewhere in the Americas region when deciding the final thresholds for “high-in” for US packaged foods and beverages.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.070
Threshold uncertainty score0.140

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0040.003
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.017
GPT teacher head0.290
Teacher spread0.273 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueNutrientsSame topicConsumer Attitudes and Food LabelingFrench-language works237,207