<i>Iron Deficiency Anemia</i> Following Prenatal Nutrition Interventions
Bibliographic record
Abstract
PURPOSE: Iron deficiency anemia (IDA) during pregnancy and infancy is still common in developed countries, especially in low-income groups. We examined the prevalence of anemia and IDA in healthy low-income pregnant women participating in the Early Childhood Initiatives (ECI) program, and in their infants when they reached six months of age. METHODS: Pregnant women were recruited by nutritionists. In mothers, hemoglobin (Hb), mean corpuscular volume, and serum ferritin (SF) were measured at 36 +/- 2 weeks of gestation. In infants, Hb, mean corpuscular volume, SF, serum iron, total iron binding capacity (TIBC), and transferrin saturation (TS) were measured at six months of age. Thirty-one mother-infant pairs participated. RESULTS: Among the 31 pregnant women participating in the ECI program, six (19.4%) were anemic (Hb <110 g/L) and five (16.1%) suffered from IDA (Hb <110 g/L and SF <10 microg/L). Among infants, seven of 23 (30.4%) were anemic (Hb <110 g/L) and five of 23 (21.7%) suffered from IDA (Hb <110 g/L plus two of the following: TIBC >60 micromol/L, SF <10 microg/L, serum iron <5.3 micromol/L, TS < or = 15%). CONCLUSIONS: The prevalence of anemia in this group of low-income pregnant women is comparable to that in privileged women. The prevalence of IDA in infants is comparable to that observed in other high-risk groups. Effective strategies are needed to prevent IDA in vulnerable groups.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".