The effect of hepatitis B virus on the risk of pregnancy outcomes: a systematic review and meta-analysis of cohort studies
Bibliographic record
Abstract
BACKGROUND: The effect of HBV on neonatal and maternal outcomes can create a basis for more accurate clinical decision-making. So, the aim of this meta-analysis is to detrmine the effect of chronic hepatitis B virus on the risk of pregnancy outcomes by combining cohort studies. METHODS: International databases in this meta-analysis included the Cumulated Index to Nursing and Allied Health Literature (CINAHL), SPORT Discuss via the EBSCO interface, PubMed (Medline), Scopus, Web of Science, Embase, which were searched up to April 2023. All cohort studies reporting the risk ratio (RR) with a 95% confidence interval (CI) were included in the study. The quality assessment was done based on the Newcastle-Ottawa Scale (NOS). RESULTS: Finally, thirty-five cohort studies were selected for meta-analysis. Outcomes of interest included pre-eclampsia, gestational diabetes, abortion, preterm birth, infant death, and other related outcomes. Results showed that the pooled RR for incident gestational diabetes in pregnant women with choronic hepatitis B infection was 1.16 (RR: 1.16; 95% CI 1.13-1.18; I-square: 92.89%; P value: 0.00). Similarly, the association between the presence of hepatitis B infection in pregnant women and the occurrence of pre-eclampsia was 1.10 (RR: 1.10; 95% CI 1.04-1.16; I-square: 92.06%; P value: 0.00). The risk of preterm delivery in pregnant women with hepatitis B infection was 1.17 times that of pregnant women without hepatitis B infection (RR: 1.17; 95% CI 1.14-1.20; I-squared: 94.32%; P value: 0.00). CONCLUSION: This meta-analysis found that hepatitis B infection during pregnancy may be associated with an increased risk of gestational diabetes, preterm delivery, pre-eclampsia, and eclampsia. However, confirmation of this association, as well as the specific biological pathways involved in the association between HBV infection and pregnancy outcomes, requires further investigation.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.021 | 0.005 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".