Changes in nonnutritive sweetener intake in a cohort of preschoolers after the implementation of Chile's Law of Food Labelling and Advertising
Bibliographic record
Abstract
BACKGROUND: The first phase of Chile's Law of Food Labelling and Advertising showed important declines in the sugar content of packaged foods, but it is unknown whether the law led to an increase in nonnutritive sweetener (NNS) intake, particularly among preschool children. OBJECTIVES: Estimate the changes in preschoolers' NNS intake after the first phase of the Chilean law. METHODS: We used 24-h dietary recalls collected in 2016 (pre-law) and 2017 (post-law) from a cohort of preschoolers (n = 875). The primary caretaker was the respondent of the recalls. Information on NNS was obtained from nutrition facts panels collected annually and linked to dietary data. We used logistic regression to estimate the changes in the proportion of preschoolers who consume NNS and two-part models to estimate the changes in mean intake. We determined the percentage of children that surpassed the acceptable daily intake (ADI) of each NNS using the National Cancer Institute method. RESULTS: The proportion of consumers of at least one NNS increased from 77.9% to 92.0% (p-value < 0.01). The mean intake increased for sucralose, aspartame, acesulfame-K and steviol glycosides (+20.3, +15.1, +6.1 and +3.3 mg/day, respectively). In addition, NNS dietary sources changed for sucralose and steviol glycosides, becoming industrialized juices and dairy beverages more relevant while tabletop NNS became less relevant. None of the children surpassed the ADI. CONCLUSIONS: NNS intake increased in preschoolers after the first phase of a national policy that promoted sugar reformulation.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".