A Narrative Review of Childhood Picky Eating and Its Relationship to Food Intakes, Nutritional Status, and Growth
Bibliographic record
Abstract
A main characteristic of children perceived as picky eaters is their tendency to avoid certain foods or food groups. The goal of this narrative review is to provide an overview of published studies that have examined whether picky eating in childhood is in fact associated with measurable differences in food and/or nutrient intakes and growth. While picky eaters appear to consume less vegetables compared to non-picky eaters, no consistent differences were observed for the intakes of other food groups or the intakes of energy, macronutrients and dietary fiber. Although, in some studies, picky eaters had lower intakes of certain vitamins and minerals, the levels consumed generally exceeded the recommended values, suggesting nutritional requirements are being met. No consistent relationship between childhood picky eating and growth status was observed, although significant differences in body weight/growth between picky and non-picky eaters were most discernible in studies where multiple defining criteria were used to identify picky eating. The research area would benefit from the adoption of a uniform definition of picky eating. More longitudinal assessments are also required to understand the long-term impact of picky eating on nutritional status and growth.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".