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P1107 ARE RECOMMENDED DIETARY ALLOWANCES ACHIEVABLE, FOR 1-YEAR OLD NEW ZEALAND CHILDREN, WITHOUT THE USE OF FORTIFIED TODDLER FOODS?

2004· article· en· W2017146028 on OpenAlexaboutno aff
Elaine Ferguson, André Briend, Nicole Darmon, Michele Devlin

Bibliographic record

VenueJournal of Pediatric Gastroenterology and Nutrition · 2004
Typearticle
Languageen
FieldMedicine
TopicNutritional Studies and Diet
Canadian institutionsnot available
Fundersnot available
KeywordsToddlerMicronutrientMedicineNutrientDietary Reference IntakeAnimal sciencePortion sizeEnvironmental healthFood groupServing sizeNeophobiaNutrient densityFortified FoodFood sciencePediatricsVitaminBiologyInternal medicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.235
Threshold uncertainty score0.467

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.042
GPT teacher head0.270
Teacher spread0.228 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2004
Admission routes1
Has abstractyes

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