Infant dietary intake is associated with weight gain from 1 to 12 months of age
Bibliographic record
Abstract
Early life nutrition can affect future health outcomes. We aimed to describe nutrient intake and growth of infants longitudinally. Healthy breastfeeding infants were recruited from the vitamin D dose response study ( NCT00381914 ) in Montreal, Canada. At 1, 3, 6, 9 and 12 mo of age, infant's diet over 3 d was recorded by mothers including test‐weighing for breast milk intake. Nutrient intake was generated (2010b Canadian Nutrient File). Infants had healthy growth, mean weight and length for age Z‐scores similar to WHO standard. On average, 88% of infants were breastfed by 6 mo and 35% by 12 mo. Energy intake increased over the year (p<0.05) and, as a % of total energy, protein and carbohydrate increased and fat declined (p<0.01). Introduction of complementary food was 5 (±1) mo and negatively correlated with energy intake (r=−0.41; p<0.01) and weight gain (r=−0.32; p<0.01) from 1 to 12 mo. This sample represents some selection bias; however, suggests timing of complementary foods may impact growth by 1 y of age. Age (mo) 1 n=104 3 n=91 6 n=78 9 n=56 12 n=51 Total energy intake, kcal/d 535 (270,782) a 536 (252,893) a 577 (289,967) a 694 (339,1217) b 875 (498,1425) c kcal/kg/d 116 (97,130) a 87 (76,102) b 73 (59,85) c 78 (62,102) b,c 94 (74,113) b,d Protein, % of total energy/d 6 (6,11) a 6 (6,11) a 7 (6,13) b 12 (6, 25) c 16 (9,26) d Fat, % of total energy/d 56 (50,56) a 56 (37,56) a 47 (22,56) b 35 (18,49) c 35 (17,47) c Carbohydrates, % of total energy/d 39 (4,46) a 39 (4,53) a 48 (5,69) b 55 (12,76) c 53 (5,70) d Nutrient intakes from 1 to 12 mo, median (range); within rows values with different superscripts indicate p<0.05. Grant Funding Source : Canadian Institutes of Health Research; Nutricia Research Foundation
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".