A narrative review of vaccine pharmacovigilance during mass vaccination campaigns: Focus on myocarditis and pericarditis after COVID‐19 mRNA vaccination
Bibliographic record
Abstract
Vaccines have had a tremendous impact on reducing the burden of infectious diseases; however, they have the potential to cause adverse events following immunization (AEFIs). Prelicensure clinical trials are limited in their ability to detect rare AEFIs that may occur in less than one per thousand individuals. While postmarketing surveillance systems have shown COVID-19 mRNA vaccines to be safe, they led to the identification of rare cases of myocarditis and pericarditis after COVID-19 vaccination that were not initially detected in clinical trials. In this narrative review, we highlight concepts of vaccine pharmacovigilance during mass vaccination campaigns and compare the approaches used in the context of myocarditis and pericarditis following COVID-19 vaccination to historical examples. We describe mechanisms of passive and active surveillance, their strengths and limitations, and how they interacted to identify and characterize the safety signal of myocarditis and pericarditis after COVID-19 mRNA vaccination. Articles were synthesized from a PubMed search using relevant keywords for articles published on vaccine surveillance systems and myocarditis and pericarditis after COVID-19 vaccination, as well as the authors' collections of relevant publications and grey literature reports. The global experience around the identification and monitoring of myocarditis and pericarditis after COVID-19 mRNA vaccination has provided important lessons for vaccine safety surveillance and highlighted its importance in maintaining public trust in mass vaccination programmes in a pandemic context.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".