Characterization of anemia and iron deficiency in 6–12 month old infants from rural India
Bibliographic record
Abstract
Determining anemia and iron deficiency (ID) prevalence estimates in infants using stringent criteria are a critical step in designing effective prevention strategies. The objective was to characterize biomarkers of iron, folate and vitamin B12 status and inflammatory marker C ‐ reactive protein (CRP) in 6–12 month old infants. We enrolled 497 infants from rural Andhra Pradesh, India as part of an interventional study of simultaneous early learning stimulation and at‐home micronutrient fortification. At baseline, blood was collected from 483 infants. ID was diagnosed based on ferritin <12 μg/L or serum transferrin receptor (sTfR) >;2.5 mg/L and ID with hemoglobin (Hb) <11 g/dL as iron deficiency anemia (IDA) and cases which did not fit into the above categories as indeterminant. Prevalence of anemia was 67%: iron replete infants accounted for 27%; IDA 52%; ID 6% and indeterminant cases 15%. The prevalence of folate and vitamin B12 deficiency was <1% and 19%, respectively. Mean folate status was comparable among groups while vitamin B12 status was significantly lower in IDA compared to normal infants. Indeterminant infants exhibited significantly lower Hb and sTfR and higher ferritin and CRP compared to normal infants. These findings suggest that IDA is highly prevalent among infants. Research support: Mathile Institute & Micronutrient Initiative
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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 | 0.001 | 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 | 0.000 |
| 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".