Determinants of anemia and iron status among pregnant women participating in the Mama SASHA Cohort Study of Vitamin A in Western Kenya: preliminary findings (624.8)
Bibliographic record
Abstract
Maternal anemia is associated with poor birth outcomes. We estimated anemia and iron status and assessed its determinants using data from 505 pregnant women attending their first antenatal care visit in Western Kenya. Iron status was assessed using ferritin and transferrin receptor and corrected for inflammation as measured by C‐reactive protein (CRP) (>5 mg/L) and α‐1‐acid glycoprotein (AGP) (>1 g/L). Anemia was assessed with Hemocue. Mean (±SD) hemoglobin was 12.09 (±1.5) g/dl and the prevalence of anemia was 22%. Median plasma ferritin was 25.72 (IQR 18.70) µg/l and the prevalence of iron deficiency was 23%. Prevalence of any inflammation (CRP and/or AGP) was 24%. Primigravida was associated with both anemia (OR (CI): 1.92 (1.24, 2.97) and iron deficiency (OR (CI): 1.70 (1.10, 2.63). Acute malnutrition (MUAC <23 cm) was associated with both anemia (OR (CI): 2.06 (1.13, 3.77) and iron deficiency (OR (CI): 2.03 (1.11, 3.71). Vitamin A deficiency was associated with anemia (OR (CI): 1.68 (1.05, 2.71), but not iron deficiency. Other potentially modifiable factors, including food insecurity, dietary diversity, maternal education level, household consumption of iron rich foods, and gestational age were not associated with anemia or iron deficiency. The prevalence of anemia and iron deficiency is moderate and additional research is needed to understand the etiology of anemia and iron deficiency in this population.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".