A Pharmacoepidemiological Approach For Evaluating The Risk Of Myocarditis And Pericarditis Following Mrna COVID-19 Vaccination
Bibliographic record
Abstract
ObjectivesThis thesis evaluates the risk of myocarditis and pericarditis (myo/pericarditis) post-mRNA COVID-19 vaccination. MethodsThree complementary approaches were employed: disproportionality analysis using Vaccine Adverse Event Reporting System (VAERS) for early safety signals, analysis of observed-to-expected reporting rates, and a systematic review and meta-analysis comparing the rate of myo/pericarditis among vaccinated relative to unvaccinated individuals. ResultsAnalysis of VAERS and observed-to-expected reporting rates identified a signal for myo/pericarditis following mRNA COVID-19 vaccination, predominantly in young males post second doses.The meta-analysis further corroborated the findings, indicating a higher risk in those who received mRNA COVID-19 vaccinations compared to unvaccinated individuals in the absence of COVID-19 infection. ConclusionThe multi-faceted approach adopted in this study indicates a potential risk of myo/pericarditis following mRNA COVID-19 vaccination.However, further research is needed to explore co-factors, genetic predisposition, and risks in special populations, to inform public health decisions and shape vaccine recommendations.A.A., and Wsu, Z.H.: 'Global reports of myocarditis following COVID-19 vaccination: A systematic review and meta-analysis', Diabetes & Metabolic
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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 | 0.032 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.010 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".