Introducing a Suite of Low-Burden Diet Quality Indicators That Reflect Healthy Diet Patterns at Population Level
Bibliographic record
Abstract
BACKGROUND: Few low-burden indicators of diet quality exist to track trends over time at low cost and with low technical expertise requirements. OBJECTIVE: The aim was to develop and validate a suite of low-burden indicators to reflect adherence to global dietary recommendations. METHODS: Using nationally representative, cross-sectional, quantitative dietary intake datasets from Brazil and the United States, we tested the association of food-group scores with quantitative consumption aligned with 11 global dietary recommendations. We updated the Healthy Diet Indicator (HDI) to include current quantifiable recommendations of the WHO (HDI-2020). We developed 3 food-group-based scores-an overall Global Dietary Recommendations (GDR) score as an indicator of all 11 recommendations composed of 2 subcomponents: GDR-Healthy, an indicator of the recommendations on "healthy" foods, and GDR-Limit, an indicator of the recommendations on dietary components to limit. We tested associations between these scores and the HDI-2020 and its respective subcomponents. We developed 9 dichotomous food-group-based indicators to reflect adherence to the global recommendations for fruits and vegetables, dietary fiber, free sugars, saturated fat, total fat, legumes, nuts and seeds, whole grains, and processed meats. We conducted receiver operating characteristic and sensitivity-specificity analyses to determine whether the dichotomous indicators were valid to predict adherence to the recommendations in both countries. RESULTS: The GDR score and its subcomponents were moderately to strongly associated with the HDI-2020 and its respective subcomponents (absolute values of rank correlation coefficients ranged from 0.55 to 0.66). Of the 9 dichotomous indicators, 8 largely met the criteria for predicting individual global dietary recommendations in both countries; 1 indicator (total fat) did not perform satisfactorily. CONCLUSIONS: Food-group consumption data can be used to indicate adherence to quantitative global dietary recommendations at population level. These indicators may be used to track progress of countries and populations toward meeting WHO guidance on healthy diets.
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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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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".