P1107 ARE RECOMMENDED DIETARY ALLOWANCES ACHIEVABLE, FOR 1-YEAR OLD NEW ZEALAND CHILDREN, WITHOUT THE USE OF FORTIFIED TODDLER FOODS?
Bibliographic record
Abstract
Introduction: Sub-optimal micronutrient status occurs among 1 year old New Zealand (NZ) toddlers. This study, therefore, investigated the minimal dietary modifications required to achieve recommended dietary allowances (RDA) in toddler diets. Methods: Linear programming analysis was used to identify the dietary modifications required to achieve the RDAs in diets consumed by 1 year old NZ children (n=187). Three sets of models examined different modification strategies. All models minimized the difference between the child’s actual diet and a modeled diet (LP diet). Model constraints ensured that LP diets provided each child’s reported energy intake and the USA/Canadian RDAs for 11 nutrients. They also allowed the portion sizes of all foods consumed to range from 0 g to twice the portion size consumed. The first set of models used this strategy alone ie, portion size modification. In the second set of models, an additional portion of red meat (19 g/d) was also allowed in the LP diet. In the third set, all cows milk consumed was replaced by fortified toddler milk. Results were analysed by examining the dietary modifications made in all feasible LP diets. Unfeasible LP diets were those which could not meet at least one of the constraints. Results: Only 7% of the actual diets (before modeling) achieved the RDAs for all nutrients. The RDA for Fe was the most difficult to achieve (only 15% of diets achieved it) followed by Ca (71% achieved it). Allowing only food portion sizes to change by up to double the amounts consumed (model 1) resulted in only 61 feasible modeled diets (33%), and for these diets, an increase in fortified breakfast cereal (59% of feasible diets), meat (64%) and/or milk (33%) with a corresponding decrease in biscuits/cakes (39%) and sweet and savory snacks (25 & 26%) were the most common changes. When either extra red meat (model 2) or fortified toddler milk (model 3) was allowed in the LP diets, the number of feasible LP diets increased to 84 for meat (45% of diets) and 139 for toddler milk (71% of diets). For the latter, 92 of the LP diets (49%) were feasible without modifications to the original food portion sizes. Conclusion: Even in a country such as NZ, it is difficult for toddler diets to achieve the RDAs without the inclusion of fortified foods. Moreover, even when all milk in the diet was fortified, close to 30% of the diets still did not achieve at least 100% of all RDAs. Nutrition planning goals, when based on the USA/Canadian RDAs, will be difficult to achieve, especially for iron, for this age group.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".