Evaluating the relationship between myocarditis and mRNA vaccination
Bibliographic record
Abstract
INTRODUCTION: Inflammatory conditions affecting the heart and surrounding tissues have been recently reported following mRNA vaccination. Evaluating trends in the epidemiology of these events and possible mechanisms related to vaccination will enhance vaccine safety surveillance and inform best practices for future vaccine campaigns. AREAS COVERED: Epidemiology of the burden of vaccine-associated myocarditis are reviewed. Key summaries of available data from public health advisory bodies and vaccine safety surveillance databases are critically reviewed. The possible biological pathways for vaccine-associated heart inflammations are introduced. A critical synthesis of available information to inform vaccine recommendations and best practices is provided. The citations were selected by the authors based on PubMed searches of the literature, national vaccine safety surveillance databases and summaries from national public health bodies. EXPERT OPINION: Myocarditis may be associated with vaccination, through several biological mechanisms. Studies have shown that live viral vaccinations can act as a trigger for hypersensitivity inflammatory reactions, but further work is required to examine how the mRNA formulation may induce these autoimmune responses. Given that the risk of these adverse events is low, and the benefit of protection against disease is so great, the receipt of mRNA vaccines is recommended.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".