Consumption of non-nutritive sweeteners by pre-schoolers of the food and environment Chilean cohort (FECHIC) before the implementation of the Chilean food labelling and advertising law
Bibliographic record
Abstract
BACKGROUND: Consumption of non-nutritive sweeteners (NNS) is becoming increasingly more frequent, particularly in the context of obesity prevention policies. The aim of this study was to describe the consumption of NNS in an ongoing cohort of pre-schoolers (4-6-year-old) before the implementation of the Chilean Food Labelling and Advertising Law, identify sociodemographic and anthropometric characteristics associated with their consumption, and describe the main dietary sources of each NNS sub-type. METHODS: In 959 low-medium income pre-schoolers from the Food and Environment Chilean Cohort (FECHIC), dietary data from a single 24-h recall was linked to NNS content information obtained from packaged foods (n = 12,233). The prevalence of NNS consumption was estimated by food source and characterized by child and maternal sociodemographic and anthropometric variables. Intakes and main dietary sources were described for the six most prevalent NNS in Chile: Sodium Cyclamate, Saccharin, Aspartame, Acesulfame Potassium, Sucralose, and Steviol glycosides. RESULTS: Sixty-eight percent of the pre-schoolers consumed at least one source of NNS on the day of the dietary recall; most of them consumed NNS from foods and beverages (n = 532), while only 12% (n = 119) also consumed table-top sweeteners. The prevalence of NNS consumption was significantly higher among children whose mothers had a high educational level compared to children whose mothers did not complete high school (p < 0.05); however, it did not differ by any other variable studied. The highest intakes of NNS were observed for Aspartame [2.5 (1.4-3.7) mg/kg per consumer], followed by Sodium Cyclamate [1.6 (1.3-2.6) mg/kg per consumer] and Steviol glycosides [1.2 (0.2-2.1) mg/kg per consumer]. Beverages were the only food group that contributed to the intake of the six NNS studied, accounting for 22% of the overall intake of Saccharine and up to 99% of Aspartame intake. CONCLUSIONS: Before the implementation of the Food Labelling and Advertising Law, NNS consumption was highly prevalent among a cohort of low-middle income Chilean pre-schoolers. Continuous monitoring of NNS consumption is essential given potential food reformulation associated with the implementation of this set of obesity-prevention policies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".