Prenatal anemia and postpartum hemorrhage risk: A systematic review and meta‐analysis
Bibliographic record
Abstract
Abstract Introduction Postpartum hemorrhage (PPH) has remained the leading cause of maternal mortality. While anemia is a leading contributor to maternal morbidity, molecular, cellular and anemia‐induced hypoxia, clinical studies of the relationship between prenatal‐anemia and PPH have reported conflicting results. Therefore, our objective was to investigate the outcomes of studies on the relationships between prenatal anemia and PPH‐related mortality. Materials and Methods Electronic databases (MEDLINE, Scopus, ClinicalTrials.gov , PROSPERO, EMBASE, and the Cochrane Central Register of Controlled Trials) were searched for studies published before August 2019. Keywords included “anemia,” “hemoglobin,” “postpartum hemorrhage,” and “postpartum bleeding.” Only studies involving the association between anemia and PPH were included in the meta‐analysis. Our primary analysis used random effects models to synthesize odds‐ratios (ORs) extracted from the studies. Heterogeneity was formally assessed with the Higgins' I2 statistics, and explored using meta‐regression and subgroup analysis. Results We found 13 eligible studies investigating the relationship between prenatal anemia and PPH. Our findings suggest that severe prenatal anemia increases PPH risk (OR = 3.54; 95% CI: 1.20, 10.4, p‐value = 0.020). There was no statistical association with mild (OR = 0.60; 95% CI: 0.31, 1.17, p‐value = 0.130), or moderate anemia (OR = 2.09; 95% CI: 0.40, 11.1, p‐value = 0.390) and the risk of PPH. Conclusion Severe prenatal anemia is an important predictive factor of adverse outcomes, warranting intensive management during pregnancy. PROSPERO Registration Number: CRD42020149184; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=149184 .
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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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.035 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".