Identifying sources of variation in added sugar intake for Alaska Native children using a hair biomarker
Bibliographic record
Abstract
Sugars from sugar-sweetened beverages (SSBs) are an important risk factor for tooth decay. The study goal was to determine if there was variation in added sugar intake across communities and between and within households. In this cross-sectional study, intakes of total sugar, added sugar, and sugar-sweetened beverages (SSBs) were estimated for 282 Alaska Native children ages 0–10 years from 131 households in three Yukon-Kuskokwim (YK) Delta communities using biomarker equations based on hair carbon and nitrogen isotope ratios previously developed for the Yup’ik population. ANOVA was used to assess associations between each predictor (community and household) and outcome (estimated total sugars, added sugars, and SSB intake). Between- and within-household variation was estimated using a linear mixed-effects model with a random intercept for households with three or more children. There was no significant difference in mean estimated total sugar (p = 0.29), added sugar (p = 0.24), or SSB intake (p = 0.40) across communities. Significant variations were observed between and within households, with within-household variation amounting to 59% of the between-household variation. Added sugar intake in Alaska Native children from the three study communities is higher than the recommended maximum, and the variation is greater within households than between households.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".