Validating Sentinel Foods in the Diet Quality Questionnaire: Insights from Two Chilean Cohorts of Pregnant Women and Children
Bibliographic record
Abstract
Background: New tools for monitoring diets, such as the Diet Quality Questionnaire (DQQ), may help reduce the costs and burden associated with traditional methods of diet assessment. However, its proposed sentinel foods require validation in target populations. This study aimed to validate the Chilean sentinel foods and describe the performance of DQQ, as well as its Global Dietary Recommendation (GDR) indicators, in two Chilean cohorts. Methods: We analyzed dietary data from 1418 pregnant women and 799 children using 24 h recalls. Foods and beverages were classified and ranked into 29 DQQ food groups. Food items that accounted for more than 95% of the total consumption within each of the 29 food groups were selected and identified as sentinels. We estimated the proportion of consumers in each food group and calculated the indicators, as well as their relationship with the consumption of ultra-processed foods (UPFs). Results: The pregnant women had a mean age of 29.1 (SD 6.6), and the children had a mean age of 6.2 (SD 0.5). The sentinel foods of six groups (grain-based sweets, other sweets, salty snacks, deep-fried food, and sweet tea/coffee/cocoa) captured under 95% of the group’s total consumption. The pregnant women had a higher frequency of consumption of staple and healthy foods, and the mean GDR score was 9.3 (SD 2.20). The children had higher consumption of unhealthy food groups, and the mean GDR score was 8.1 (SD 2.05). The GDR-protect scores showed a moderate but statistically significant negative correlation with ultra-processed food consumption (p-value < 0.0001). Conclusions: The DQQ is a suitable tool for collecting dietary data to estimate diet quality using food group-based indicators. Additionally, it is possible to identify different dietary patterns at a crucial stage of life, such as childhood and pregnancy. However, it requires some adaptations of sentinel foods and further testing on other populations before it can be implemented to monitor Chilean 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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".