Estimating the prevalence of iron deficiency in the first two years of life: technical and measurement issues
Bibliographic record
Abstract
National-level data on iron deficiency is not available for most countries and many rely on the prevalence of anemia as a proxy estimate, assuming that approximately 50% of anemia cases are caused by iron deficiency. Anemia, however, has multiple causal factors and the risk attributable to any one cause will depend on its relative importance in a population in relation to other causes. The present review summarizes current estimates on the prevalence of iron deficiency and anemia in children younger than 2 years and evaluates the strengths and weaknesses of currently available indicators of iron deficiency in children. Anemia prevalence is insufficient to estimate the prevalence of iron deficiency in children younger than 2 years. The methods widely used to assess iron deficiency at the population level rely on venous blood samples and are complicated and costly to implement.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".