Hepatitis B virus infection during pregnancy and the risk of postpartum hemorrhage: a protocol for systematic review and meta-analysis
Bibliographic record
Abstract
Background: Hepatitis B virus (HBV) infection is a significant public health issue worldwide, with a hepatitis B surface antigen (HBsAg) seroprevalence of 3.5%. Maternal HBV infection during pregnancy, a common comorbidity, is associated with an increase in the risk of adverse obstetric and perinatal outcomes. However, the relationship between maternal HBV infection and postpartum hemorrhage (PPH), a leading contributor to maternal morbidity and mortality, is currently uncertain. The aim of this study is to comprehensively clarify the potential impact of maternal HBV on PPH risk. Methods and Analysis: The authors initially searched five English databases and three Chinese databases from their inception to 26th June 2023. Two reviewers will independently conduct study selection, data extraction, and quality assessment. Cohort and case–control studies investigating the effect of maternal HBV infection on PPH will be included, with study quality assessed using the Newcastle–Ottawa Scale (NOS). Meta-analyses will be performed using a fixed-effects model for I 2≤50% or a random-effects model otherwise. Several categories of subgroup analyses (e.g. sample size more than 1000 vs. less than 1000) and sensitivity analyses (e.g. omit NOS scores less than 7) will be conducted, and publication bias will be assessed through funnel plots, Begg’s and Egger’s tests using STATA 18.0. Ethics and Dissemination: This systematic review and meta-analysis do not require ethics approval and the results will be published in peer-reviewed journals. The findings of this systematic review will provide evidence on the impact of maternal HBV infection on PPH, which will contribute to better prevention and management of PPH in clinical practice and a better understanding of the disease burden of HBV infection. PROSPERO registration number: CRD42023442626
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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 | 0.061 | 0.099 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.018 | 0.024 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.058 | 0.005 |
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".