Fructose intake and food sources in West Australian adolescents
Bibliographic record
Abstract
Aim: This research aimed to cross-sectionally quantify fructose consumption and identify major food sources of fructose in adolescents participating in the 14-year follow up of the Western Australian Pregnancy Cohort (Raine) Study. Methods: Subjects were 822 adolescents aged 13-15 years participating in the Raine Study. Dietary intake was assessed by 3-day food records and entered in the FoodWorks dietary analysis program. Total fructose values for individual foods were linked from the Nutrient Tables for use in Australia, the University of Minnesota Nutrition Coordinating Centre Food and Nutrient Database, and the Canadian Nutrient File. Results: Fructose contributed 9.1% of total energy intake for the group. Boys reported higher absolute fructose intakes than girls (58.9 g 26.6 g vs 48.3 g 20.1 g, respectively, P < 0.001), while girls had higher energy adjusted fructose intakes than boys (55.7 g 16.1 g vs 51.8 g 20.2 g, respectively, P = 0.002). Major food sources of total fructose were beverages, in particular soft drinks, followed by fruit and confectionery. No significant associations were found between fructose intake and level of physical activity, Body Mass Index or socioeconomic status indicators in unadjusted analyses; however, adolescents from higher socioeconomic groups consumed more fructose from fruit, whereas adolescents from lower socioeconomic groups consumed more fructose from beverages. Conclusions: To our knowledge, this is the first study to describe fructose intake and food sources in Australian adolescents. Results are similar to those previously reported in studies of US adolescents.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".