Longitudinal Meta-cohort study protocol using systems biology to identify vaccine safety biomarkers
Bibliographic record
Abstract
The International Network of Special Immunization Services (INSIS) was established to investigate the causes and risk factors of rare adverse events following immunizations (AEFIs) and develop immunization strategies for mitigating or preventing risk for individuals with prior AEFIs or at risk of AEFIs. INSIS integrates clinical data with multi-omic technologies (e.g., transcriptomics, proteomics, metabolomics) through a global consortium of clinical networks, leading immunology, pharmacogenomics teams to uncover the molecular mechanisms behind AEFIs. The network ensures accurate and standardized data collection and analysis through rigorous data management and quality assurance processes. INSIS also implements harmonized case definitions and protocols for collecting data and samples related to rare AEFIs, such as myocarditis, pericarditis, and Vaccine-Induced Immune Thrombocytopenia and Thrombosis (VITT) after COVID-19 vaccinations. This protocol outlines the comprehensive approach to enhance risk-benefit assessments of vaccines across populations, identify actionable biomarkers to inform discovery and development of safe vaccines, and support personalized vaccination strategies.
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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 | 0.031 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.028 | 0.004 |
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".