Screening for Iron Deficiency in Early Childhood Using Serum Ferritin in the Primary Care Setting
Bibliographic record
Abstract
OBJECTIVES: The American Academy of Pediatrics recommends universal screening for anemia using hemoglobin at 12 months. However, hemoglobin lacks diagnostic accuracy for iron deficiency, and the optimal age for screening has not been determined. Our objective was to assess a screening strategy for iron deficiency using serum ferritin. METHODS: We conducted a cross-sectional study of children 1 to 3 years old attending a health supervision visit. We examined the relationship between child age and serum ferritin, age and hemoglobin, hemoglobin and serum ferritin, and the prevalence of elevated C-reactive protein (CRP). RESULTS: < .0001). A linear spline model revealed that from 12 to 15 months, for each 1-month increase in age, serum ferritin levels decreased by 9% (95% confidence interval [CI]: 5 to 13). From 15 to 24 months, the rate of change was nonsignificant. From 24 to 38 months, for each month increase in age, serum ferritin increased by 2% (95% CI: 1 to 2). For hemoglobin, from 12 to 24 months, the rate of change was nonsignificant. From 24 to 38 months, for each 1-month increase in age, hemoglobin increased by 20% (95% CI: 9 to 32). Compared with the serum ferritin cutoff of <12 μg/L, the hemoglobin cutoff of <110 g/L had a sensitivity of 25% (95% CI: 19 to 32) and a specificity of 89% (95% CI: 87 to 91). Elevated CRP ≥10 mg/L occurred in 3.3% (95% CI: 2.5 to 4.2). CONCLUSIONS: Screening for iron deficiency using serum ferritin at 15 or 18 months may be a promising strategy. For children at low risk for acute inflammation, concurrent measurement of CRP may not be necessary.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".