Chronic hepatitis B in pregnancy: unique challenges and opportunities
Bibliographic record
Abstract
Chronic hepatitis B (CHB) affects 350 million individuals worldwide. Perinatal transmission leads to high rates of chronic infection and complications, including cirrhosis and hepatocellular carcinoma. It is important to recognize and appropriately treat CHB in pregnancy, thereby reducing the risk of neonatal transmission and HBV-associated morbidity and mortality. Screening for CHB is recommended in all pregnant mothers as is universal vaccination of infants with hepatitis B virus (HBV) vaccine with or without hepatitis B immunoglobulin (HBIG). This has resulted in a lower incidence of HBsAg seropositivity and HCC in regions where universal infant vaccination has been endorsed. Mode of delivery and breastfeeding do not appear to affect HBV transmission rates based on available data. Overall, CHB does not increase perinatal maternal-fetal mortality. Administration of oral antiviral therapy during the third trimester to HBsAg-positive mothers with HBV DNA≥7 log IU/mL may be useful in preventing breakthrough infection. Treatment may be considered earlier in pregnancy for persistently active liver disease shown by high ALT, HBV DNA levels and/or significant hepatic fibrosis. Lamivudine, tenofovir and telbivudine are safe and effective and are the agents of choice in pregnancy. However, further clinical studies are necessary to elucidate the role of antiviral therapy in the pregnant HBV carrier.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".