The association between placenta abruption and congenital anomalies: A systematic review and meta-analysis
Bibliographic record
Abstract
Background Congenital abnormalities (CAs) are a significant cause of death and disability among children worldwide. Identifying risk factors can help reduce the incidence and mortality associated with CAs. This study aimed to explore the relationship between placental abruption (PA) and congenital abnormalities (CAs). Methods A systematic search was conducted in PubMed (Medline), Web of Science, Scopus, and Science Direct, up to July 10, 2025. The analysis utilized a random-effects model. To evaluate heterogeneity among the studies, we applied the chi-square test (χ 2 ) and the I 2 statistic. Additionally, regression tests, including Egger’s and Begg’s tests, were carried out to assess publication bias. The quality of observational studies was evaluated using the modified Newcastle–Ottawa Scale (NOS). A p-value of less than 0.05 was considered statistically significant, and the analysis was performed using Stata software, version 13. Results In total, six studies met the inclusion criteria and were included in this systematic review. A significant association was found, with PA increasing the risk of CAs (OR = 2.83; 95% CI: 1.88 to 3.79). Substantial heterogeneity was observed among the studies (I 2 = 91.9%, P < 0.001). After subgrouping, a significant association between PA and the risk of CAs was observed in both case-control studies (OR = 1.95, 95% CI: 1.51 to 2.38; I 2 = 0.0%, P = 0.523) and cohort studies (OR = 3.28, 95% CI: 1.68 to 4.88; I 2 = 95.1%, P < 0.001). Notably, homogeneity was found among the case-control studies. Conclusion Our study demonstrates a significant positive association between PA and CAs, but causality cannot be inferred. Thus, it is suggested to monitor for CAs in the fetus of mothers with PA.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.035 |
| Bibliometrics | 0.009 | 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) | 0.004 | 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".