Vegetarian Diet, Growth, and Nutrition in Early Childhood: A Longitudinal Cohort Study
Bibliographic record
Abstract
OBJECTIVES: The primary objective of this study was to examine the relationships between vegetarian diet and growth, micronutrient stores, and serum lipids among healthy children. Secondary objectives included exploring whether cow's milk consumption or age modified these relationships. METHODS: A longitudinal cohort study of children aged 6 months to 8 years who participated in the TARGet Kids! cohort study. Linear mixed-effect modeling was used to evaluate the relationships between vegetarian diet and BMI z-score (zBMI), height-for-age z-score, serum ferritin, 25-hydroxyvitamin D, and serum lipids. Generalized estimating equation modeling was used to explore weight status categories. Possible effect modification by age and cow's milk consumption was examined. RESULTS: A total of 8907 children, including 248 vegetarian at baseline, participated. Mean age at baseline was 2.2 years (SD 1.5). There was no evidence of an association between vegetarian diet and zBMI, height-for-age z-score, serum ferritin, 25-hydroxyvitamin D, or serum lipids. Children with vegetarian diet had higher odds of underweight (zBMI <-2) (odds ratio 1.87, 95% confidence interval 1.19 to 2.96; P = .007) but no association with overweight or obesity was found. Cow's milk consumption was associated with higher nonhigh-density lipoprotein cholesterol (P = .03), total cholesterol (P = .04), and low-density lipoprotein cholesterol (P = .02) among children with vegetarian diet. However, children with and without vegetarian diet who consumed the recommended 2 cups of cow's milk per day had similar serum lipids. CONCLUSIONS: Evidence of clinically meaningful differences in growth or biochemical measures of nutrition for children with vegetarian diet was not found. However, vegetarian diet was associated with higher odds of underweight.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".