Impact of hepatitis B birth dose on immune response in Pakistani children: an open-label, non-inferiority randomized controlled trial, implications for achieving SDG target
Bibliographic record
Abstract
Background Despite presence of hyperendemic areas, the national immunisation schedule in Pakistan does not include a hepatitis B birth dose, placing newborns at an additional risk of acquiring hepatitis B. This study aimed to assess the impact of adding hepatitis B birth dose in existing national vaccination schedule.Methods An open label, randomised controlled non-inferiority trial enrolled 296 healthy near-term mothers to intervention and control groups. Newborns in the intervention group received a hepatitis B birth dose along with routine immunisation vaccines, while control group newborns received vaccinations under the national schedule. Seroprotection was measured and compared at birth and 8 weeks after administering the third dose of pentavalent vaccine. The risk ratio of seroprotection was computed and compared with the delta value set at 5%.Results The study found that 95.8% of infants in the intervention group achieved seroprotection, which was significantly higher than the control group’s 58.7%. The difference in risk ratio of seroprotection was 1.62 (CI95: 1.37–1.93), with the upper limit of the CI below the delta margin, confirming non-inferiority. The time interval between birth and the first hepatitis B immunisation shot was a predictor of seroprotection, with an odds ratio of 1.79 (CI95: 1.01–2.9).Conclusion Our study indicates that adding a hepatitis B birth dose to the immunisation schedule in Pakistan is non-inferior to the existing one. This can also contribute towards Pakistan’s achievement of the SDG target of reducing hepatitis B surface antigen seroprevalence in children under 5 years of age.Trial Registration Number NCT04870021
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 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".