Anemia during pregnancy and adverse pregnancy outcomes: a systematic review and meta-analysis of cohort studies
Bibliographic record
Abstract
Objectives: Anemia in pregnancy has been a topic of interest for researchers due to its potential impact on various adverse pregnancy outcomes. This study aims to explore the relationship between anemia and adverse pregnancy outcomes such as preterm birth, low birth weight, and maternal mortality. Methods: We conducted both a systematic review and a meta-analysis on the associations between anemia during pregnancy and adverse pregnancy outcomes. We searched Chinese databases (CNKI, Wanfang, CBM, VIP) and English ones (Cochrane Library, PubMed, Embase, Web of Science). Two researcher-authors independently assessed study quality with the Newcastle-Ottawa Scale. After extracting data, we analyzed heterogeneity and used a random-effects model for higher heterogeneity and a fixed-effects model for low heterogeneity in the meta-analysis while also systematically synthesizing and narratively describing findings in the systematic review. Results: A total of 31 cohort studies were included. Meta-analysis showed that the risk of postpartum hemorrhage [RR [95% CI], 2.76 [1.63, 4.66]], premature rupture of membranes (PROM) [1.94 (1.26, 3.00)], preterm delivery [1.51 (1.33, 1.72)], low birth weight (LBW) [1.40 (1.19, 1.63)], cesarean section[1.33 (1.02, 1.74)], gestational hypertension[1.28 (1.14, 1.44)] and neonatal asphyxia[1.21 (1.07, 1.37)] was higher in the group of anemia in pregnancy than in the control group. Conclusion: Maternal anemia is associated with an increased risk of seven adverse pregnancy outcomes: postpartum hemorrhage, PROM, preterm delivery, LBW, cesarean section, gestational hypertension and neonatal asphyxia. Appropriate nutritional supplementation and screening for anemia before and during pregnancy are recommended to improve maternal health and manage adverse pregnancy outcomes.
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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 | 0.019 | 0.049 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.042 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".