A systematic review and meta-analysis of the correlation between maternal and neonatal iron status and haematologic indices
Bibliographic record
Abstract
BACKGROUND: Iron deficiency (ID) is the leading single-nutrient deficiency in the world. Anaemia is a common outcome of ID that affects half of pregnancies worldwide with serious consequences for child development. Whether haematologic indices and biomarkers of iron status in pregnant women correlate with those of their neonates is unclear. This systematic review evaluated studies comparing haematologic and iron status indices in pregnant women and their newborns/neonates. METHODS: We searched MEDLINE, EMBASE, CINAHL, and Web of Science from database inception until March 2020 for primary studies comparing haematologic and iron status indices between women and their newborns up to 48 h after birth. We summarized the results descriptively and calculated pooled correlation coefficients in mothers and newborns/neonates using the Schmidt-Hunter method. The protocol was registered at PROSPERO International Prospective Register of Systematic Reviews (Registration number: CRD42018093094). FINDINGS: Sixty-five studies were included. Pooled correlation coefficients for biomarkers of iron status in mothers and newborns/neonates were 0.13 (ferritin), 0.42 (hepcidin), 0.30 (serum/plasma iron), 0.09 (transferrin), 0.20 (transferrin saturation), and 0.16 (total iron binding capacity). Pooled correlation coefficients for haematological indices in mothers and newborns/neonates were 0.15 (haemoglobin), 0.15 (haematocrit), 0.25 (mean cell/corpuscular haemoglobin), 0.22 (mean cell/corpuscular volume). INTERPRETATION: Maternal biomarkers of iron and haematologic status correlate poorly with those in newborns/neonates. These results underscore a need for alternative approaches to estimate foetal/neonatal iron status and haematological indices. FUNDING: MBO and SLB hold Canada Research Chairs, and grants from the Women and Children's Health Research Institute and Canadian Institutes of Health Research.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.012 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".