Maternal ethnicity and iron status in early childhood in Toronto, Canada: a cross-sectional study
Bibliographic record
Abstract
OBJECTIVES: This study aimed to evaluate the association between maternal ethnicity and iron deficiency (ID) in early childhood, and to evaluate whether infant feeding practices linked to ID differ between maternal ethnic groups. METHODS: This was a cross-sectional study of healthy children 1-3 years of age. Adjusted multivariable logistic regression analyses were used to evaluate the association between maternal ethnicity and ID (serum ferritin <12 µg/L) and the association between maternal ethnicity and five infant feeding practices (breastfeeding duration; bottle use beyond 15 months; current formula use; daily cow's milk intake >2 cups; meat consumption). RESULTS: Of 1851 children included, 12.2% had ID. Compared with the European referent group, we found higher odds of ID among children of South Asian and West Asian/North African maternal ethnicities, and lower odds of ID among children of East Asian maternal ethnicity. Statistically significant covariates associated with higher odds of ID included longer breastfeeding duration and daily cow's milk intake >2 cups. Current infant formula use was associated with lower odds of ID. Children of South Asian maternal ethnicity had higher odds of bottle use beyond 15 months of age and lower odds of meat consumption. CONCLUSIONS: We found increased odds of ID among children of South Asian and West Asian/Northern African maternal ethnicities. We found a higher odds of feeding practices linked to ID in children of South Asian maternal ethnicity, but not in children of West Asian/North African maternal ethnicity. Culturally tailored approaches to providing guidance to parents on healthy infant feeding practices may be important to prevent ID in early childhood. TRIAL REGISTRATION NUMBER: NCT01869530.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".