Factors Affecting the Long-Term Protection Against Hepatitis B Immunization in Infancy: A Meta-Analysis
Bibliographic record
Abstract
Abstract: \nIntroduction: Hepatitis B virus (HBV) infection is a major global health issues and one of the most dangerous viral infections with a high mortality rate. Newborns and infant vaccination against chronic HBV infection are crucial for preventing mother-to-child transmission (MTCT). This study aimed to conduct a meta-analysis to investigate the factors affecting long-term protection against Hepatitis B Immunization in infancy. Material and Methods: Our literature searches are from PubMed, Science Direct, Web of Science, and ProQuest publications between January 2000 and December 2021. The included literature assessed the risk of bias using the Newcastle Ottawa Quality Assessment Scale. We identify Hepatitis B surface antibodies (anti-HBs) ≥ 10 mIU/mL as being protective against HBV infection. The results are combined with a random effect or fixed effect model. Results: Eighteen eligible observational studies with a total of 16,642 participants were included. Analysis of factors affecting long-term protection status by assessing anti-HBs titers showed significant results on several factors, including gestational age for anti-HBs titers (OR 2.5; 95% CI 1.62-3.85; p<0.0001), weight for age to anti-HBs titers (OR 1.36; 95% CI 1.06-1.75; p=0.02), length for age to anti-HBs titers (OR 0.01; 95% CI 0.01-0.02; p<0.00001), and immunization status based on the number of vaccine doses (4 doses vs 3 doses) to anti-HBs titers (p<0.00001). Conclusions: Anti-HBs titers of hepatitis B immunization were significantly affected by gestational age, weight for age, length for age, and vaccine doses. Parents of newborns must be informed about basic immunization and provide adequate nutritional intake to the mother and babies to prevent HBV infection.
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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 | 0.013 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.051 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".