Real‐World Study for Mother‐To‐Child Transmission of Hepatitis B in Guangdong Province, 2021–2023
Bibliographic record
Abstract
This study aimed to evaluate the status of hepatitis B virus (HBV) mother-to-child transmission (MTCT) among infants born to hepatitis B surface antigen (HBsAg)-positive mothers from 2021 to 2023 and identify key factors influencing HBV MTCT, providing critical insights to inform future HBV prevention strategies. Data were obtained from the case records of HBsAg-positive pregnant women and their newborns in Guangdong Province from January 1, 2021, to December 31, 2023. For HBsAg and anti-HBs positive rates, bivariate analysis was conducted to examine associations between maternal and infant characteristics. Additionally, Firth's bias reduction logistic regression analysis was conducted to identify factors influencing MTCT. Our study analysed data from 131,781 HBsAg-positive pregnant women and their newborns in Guangdong Province from 2021 to 2023. Among 131,781 infants completing PVST, the overall HBV MTCT rate was 0.44%, with an anti-HBs positivity rate of 93.48%. Maternal age, administration of HepB-BD and HBIG and use of antiviral treatment during pregnancy were significantly associated with HBV MTCT. However, birth weight, maternal education level, mode of delivery and number of births were not significantly associated with HBV MTCT risk. This study provides a comprehensive analysis of HBV MTCT among HBsAg-positive pregnant women and their infants in Guangdong Province from 2021 to 2023. Despite significant advancements in HBV MTCT prevention, our findings underscore the need for enhanced strategies, particularly for pregnant women with high HBV viral loads. Strengthening maternal antiviral treatment, ensuring timely and comprehensive infant follow-up, and implementing targeted health education programs will be essential for further reducing MTCT rates and improving long-term 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".