Diet Quality and Cognitive Performance in Children Born Very Low Birth Weight
Bibliographic record
Abstract
Children born very low birth weight (VLBW, <1,500 g) are at high risk for cognitive and academic difficulties later in life. Although early nutrition (e.g., breastfeeding) is positively correlated with IQ in children born VLBW, the association between dietary intake in childhood and cognitive performance is unknown. Thus, our study is the first to investigate the relationship between diet quality, as measured by the Healthy Eating Index-2010 (HEI-2010) and cognitive performance in a Canadian cohort of 5-year-old children born VLBW (n = 158; 47% female). Diet quality was measured using two 24-h diet recalls obtained from parents and cognitive performance was assessed using the Wechsler Preschool and Primary Scale of Intelligence-IV (WPPSI-IV). To account for additional sociodemographic factors that could influence neurodevelopment, linear regression analyses were adjusted for sex, household income above/below the poverty line, maternal education, birth weight and breastfeeding duration. Mean ± SD HEI-2010 score was 58.2 ± 12.4, with most children (67%) having diets in “need of improvement” (scores 51–80). HEI-2010 scores were not significantly associated with IQ or any other WPPSI-IV composite score. Significant predictors of IQ in our model were birth weight, sex, and maternal education. Our findings emphasize the important role of maternal education and other sociodemographic factors on neurodevelopment in children born VLBW. Further, despite not finding any significant association between HEI-2010 scores and IQ, our results highlight the need to improve diet quality in young children born VLBW. Further research is needed to confirm the impact of diet quality on cognitive performance in this vulnerable population.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".