Iron status of young children from immigrant families
Bibliographic record
Abstract
OBJECTIVES: Children from immigrant families may be at risk for iron deficiency (ID) due to differences in pre-migration and post-migration exposures. Our objectives were to determine whether there is an association between family immigrant status and iron stores and to evaluate whether known dietary, environmental or biological determinants of low iron status influence this relationship. DESIGN: This was a cross-sectional study of healthy urban preschool children (12-72 months) recruited from seven primary care practices in Toronto. Laboratory assessment of serum ferritin and haemoglobin and standardised parent-completed surveys were completed between 2008 and 2013 during routine health maintenance visits. Multiple regression analyses were used to evaluate the association between family immigrant status and serum ferritin, ID (ferritin <14 μg/L) and iron deficiency anaemia (IDA) (ferritin <14 μg/L and haemoglobin ≤110 g/L). RESULTS: Of 2614 children included in the analysis, 47.6% had immigrant family status. The median serum ferritin was 30 μg/L and 10.4% of all children had ID and 1.9% had IDA. After adjusting for maternal ethnicity and education, age, sex, income quintile, cow's milk intake, breastfeeding duration and bottle use, there were no significant associations between immigrant status and ferritin, ID or IDA. Significant predictors of low iron status included age, sex, cow's milk intake and breastfeeding duration. CONCLUSIONS: We found no association between family immigrant status and iron status after including clinically important covariates in the models. These data suggest immigrant children may not need enhanced screening for iron status or targeted interventions for iron supplementation.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".