Biomarkers of vaccine safety and efficacy in vulnerable populations: Lessons from the fourth international precision vaccines conference
Bibliographic record
Abstract
Vaccination has been a cornerstone of public health, substantially reducing the global burden of infectious diseases, notably evident during the COVID-19 pandemic caused by SARS-CoV-2. However, vulnerable populations (VPs), including those in extreme age groups and those with underlying health conditions, have borne a disproportionate burden of morbidity and mortality from infectious diseases. Understanding vaccine immunogenicity in these populations is crucial for developing effective vaccines. Characterizing vaccine responses in VPs presents unique challenges due to under-vaccination, sub-optimal vaccine responses, and distinct mechanisms of vaccine-induced protection. To address these challenges, experts convened at the 4th International Precision Vaccines Conference in Rome. Co-hosted by the Precision Vaccines Program of Boston Children's Hospital and Ospedale Pediatrico Bambino Gesù, the conference focused on biomarkers of vaccine safety and efficacy in vulnerable populations. Discussions at the conference emphasized the need for multidisciplinary strategies and international collaborations to optimize vaccine development. Key areas of focus included assessing vaccine safety, defining biomarkers for vaccine immunogenicity, developing human in vitro assay models, and accelerating the selection of novel vaccine formulations and adjuvants tailored for vulnerable populations. The conference provided a platform for experts from diverse fields, including immunology, paediatrics, and vaccinology, to exchange ideas and advance research in precision vaccines. This manuscript highlights key concepts discussed at the conference and underscores the importance of precision vaccines in addressing the unique needs of vulnerable populations.
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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 | 0.029 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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".