SCREENING FOR IRON DEFICIENCY IN EARLY CHILDHOOD USING SERUM FERRITIN
Bibliographic record
Abstract
BACKGROUND: Iron deficiency peaks in prevalence in early childhood and is associated with poor neurodevelopmental outcomes which may be irreversible. The American Academy of Pediatrics recommends universal screening for anemia with measurement of hemoglobin at 12 months of age. There are two limitations to this strategy. First, hemoglobin lacks sensitivity and specificity for iron deficiency. Second, the optimal age for screening has not been studied. Our objective was to examine a screening strategy using serum ferritin (SF) and to determine the relationship between child age and SF levels. OBJECTIVES: Our objective was to examine a screening strategy using serum ferritin (SF) and to determine the relationship between child age and SF levels. DESIGN/METHODS: Healthy children 12-36 months of age were recruited from a primary care research network. Blood samples for SF and C-reactive protein (CRP) were obtained during scheduled health supervision visits at 12, 15, 18, 24, or 36 months. We excluded children with CRP ≥10 mg/L. Restricted cubic spline (RCS) regression analysis was performed to test for a non-linear relationship between age and SF. Linear spline models were used to examine the rate of change between each age. Mean SF levels and the proportion of children with SF levels <12 µg/L were calculated for each age. RESULTS: 1470 children met eligibility and were included in the analysis; 48% were females. 28 children (2%) were excluded due to elevated CRP. The RCS regression analysis confirmed a U-shaped non-linear relationship between age and SF. SF was highest at 12 months, reached the lowest inflection point at approximately 20 months, and rose again to 36 months. The linear spline models showed that from 12-15 months, for each 1-month increase in age, SF decreased by 11.5% (p<0.0001); the rate of change was not significant from 15-18 months (p=0.3) or from 18-24 months (p=0.3); from 24 to 36 months, for each 1-month increase in age, SF increased by 1.95% (p<0.0001). At each age, the SF mean (± SD) and proportion with <12 µg/L were:
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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".