Iron status and risk factors of iron deficiency among pregnant women in Singapore: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Iron deficiency is the most prevalent nutrient deficiency and the most common cause of anaemia worldwide. Because of the increased iron requirements during pregnancy, iron deficiency can lead to maternal anaemia and reduced newborn iron stores. We examined the proportion and risk factors of iron deficiency among pregnant women in a developed Asian country. METHODS: Within a prospective cohort in Singapore, 985 Asian women were assessed for iron status at 26-28 weeks' gestation, with plasma ferritin and soluble transferrin receptor (sTfR) measurements. Iron status was determined according to plasma ferritin concentrations at ≥30 μg/L (iron sufficiency), 15 to < 30 μg/L (modest iron depletion) and < 15 μg/L (severe iron depletion). Multivariable ordinal logistic regression was used to analyze risk factors for modest and severe iron depletion. RESULTS: The median (25-75th percentile) plasma ferritin concentration was 24.2 (19.9-30.6) μg/L. Overall, 660 (67.0%) and 67 (6.8%) women had modest and severe iron depletion, respectively. Higher plasma sTfR was observed in women with severe iron depletion than among those with iron sufficiency (median 17.6 versus 15.5 nmol/L; p < 0.001). Age < 25 years (odds ratio 2.36; 95% confidence interval 1.15-4.84), Malay (2.05; 1.30-3.24) and Indian (1.98; 1.14-3.44) ethnicities (versus Chinese), university qualification (1.64; 1.13-2.38), multiparity (1.73; 1.23-2.44) and lack of iron-containing supplementation (3.37; 1.25-8.53) were associated with increased odds of modest and severe iron depletion. CONCLUSIONS: Nearly three-quarters of Singaporean women were iron deficient in the early third trimester of pregnancy. These results suggest universal screening and supplementation of at-risk pregnancies may be evaluated as a preventive strategy. TRIAL REGISTRATION: NCT01174875 . Registered 1 July 2010 (retrospectively registered).
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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 | 0.002 | 0.000 |
| 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) | 0.000 | 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".