Modeling the Long-term Persistence of Hepatitis A Antibody After a Two-Dose Vaccination Schedule in Argentinean Children
Bibliographic record
Abstract
BACKGROUND: Long-term seroprotection data are essential for decision-making on the need and timing of vaccine boosters. Based on data from longitudinal serological studies, modeling can provide estimates on long-term antibody persistence and inform such decision-making. METHODS: We examined long-term anti-hepatitis A virus (anti-HAV) antibody persistence in Argentinean children ≤15 years after the initial study where they completed a 2-dose course of inactivated hepatitis A vaccine (Avaxim 80U Pediatric, Sanofi Pasteur, Lyon, France). Blood serum samples were taken at baseline, 2 weeks (post first dose), 6 months (pre-booster), 6.5 months (post-booster), 10 years and 14-15 years after first vaccine dose. We fitted 8 statistical model types, predominantly mixed effects models, to anti-HAV persistence data, to identify the most appropriate and best fitting models for our data set and to predict individuals' anti-HAV levels and seroprotection rates up to 30 years post vaccination. RESULTS: Fifty-four children (mean age at enrollment 30.4 months) were enrolled up to 15 years post first vaccine dose. There were 3 distinct periods of antibody concentration: rapid rise up to peak concentration post-booster, rapid decay from post-booster to 10 years, followed by slower decay. A 3-segmented linear mixed effects model was the most appropriate for the data set. Extrapolating based on the available 14-15-year follow-up, the analysis predicted that 88% of individuals anti-HAV seronegative prior to vaccination would remain seroprotected at 30 years post vaccination and lifelong seroprotection for vaccinees seropositive prior to vaccination. CONCLUSIONS: Currently available data demonstrate that Avaxim 80U Pediatric confers to most vaccinees a high level of seroprotection against hepatitis A infection for at least 20-30 years.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".