Prenatal Hemoglobin Concentration and Long-Term Child Neurocognitive Development
Bibliographic record
Abstract
Anemia in pregnancy, defined by a hemoglobin level (Hb) of less than 110 g/L, contributes to infant mortality and morbidity in sub-Saharan Africa. Maternal Hb changes physiologically and pathologically during pregnancy. However, the impact of these changes on long-term child neurocognitive function is unknown. This study therefore investigates the association between Hb at specific antenatal care visits and prenatal Hb trajectories during pregnancy and long-term child neurocognitive function. We analyzed data from a prospective cohort study that included 6-year-old singleton children born to women enrolled before 29 weeks of gestation into an antimalarial drug clinical trial. Hemoglobin level was analyzed from venous blood collected at least twice during pregnancy and at delivery. We used group-based trajectory modeling to identify distinct prenatal Hb trajectories. In total, 478 children (75.1% of eligible children) had assessment of cognitive and motor functions at 6 years of age. Three distinct Hb trajectories were identified: persistently anemic (Hb <110 g/L throughout the second and third trimesters), anemic to nonanemic (Hb <110 g/L at second trimester with increasing Hb toward the third trimester to Hb ≥110 g/L), and persistently nonanemic (Hb ≥110 g/L throughout the second and third trimesters). Children of women in the persistently anemic and anemic-to-nonanemic groups had significantly lower neurocognitive scores than children of women in the persistently nonanemic group (β = -6.8, 95% CI: -11.7 to -1.8; and β = -6.3, 95% CI: -10.4 to -2.2, respectively). The study shows that maintaining an elevation of Hb at or above 110 g/L from the second to third trimester of pregnancy may be associated with optimal long-term child neurocognitive function.
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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 | 0.003 |
| 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".