Regional differences in milk and complementary feeding patterns in infants participating in an international nutritional type 1 diabetes prevention trial
Bibliographic record
Abstract
Differences in breastfeeding, other milk feeding and complementary feeding patterns were evaluated in infants at increased genetic risk with and without maternal type 1 diabetes (T1D). The Trial to Reduce IDDM in the Genetically at Risk is an international nutritional primary prevention double-blinded randomized trial to test whether weaning to extensively hydrolyzed vs. intact cow's milk protein formula will decrease the development of T1D-associated autoantibodies and T1D. Infant diet was prospectively assessed at two visits and seven telephone interviews between birth and 8 months. Countries were grouped into seven regions: Australia, Canada, Northern Europe, Southern Europe, Central Europe I, Central Europe II and the United States. Newborn infants with a first-degree relative with T1D and increased human leukocyte antigen-conferred susceptibility to T1D were recruited. A lower proportion of infants born to mothers with than without T1D were breastfed until 6 months of age in all regions (range, 51% to 60% vs. 70% to 80%). Complementary feeding patterns differed more by region than by maternal T1D. In Northern Europe, a higher proportion of infants consumed vegetables and fruits daily compared with other regions. Consumption of meat was more frequent in all European regions, whereas cereal consumption was most frequent in Southern Europe, Canada and the United States. Maternal T1D status was associated with breastfeeding and other milk feeding patterns similarly across regions but was unrelated to the introduction of complementary foods. Infant feeding patterns differed significantly among regions and were largely inconsistent with current recommended guidelines.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".